1,721,084 research outputs found
Hierarchical Fuzzy Petri Nets And α-level Sets Inference
No full textThe work developed in this paper focuses the representations of α-level sets inference using the Hierarchical High Level Fuzzy Petri Nets. The basic hierarchical construct used is the substitution of transitions. The modeling process is carried out defining one non-hierarchical HLFPN called page, that models the general operation and having different instances of the page for each one of the intervals representing the α sets. The concepts of HLFPN and Hierarchical HLFPN proposed earlier are reviewed, as well as the ability to model fuzzy rule based systems in a compact form provided by the hierarchical constructs and pages. The inference method based on α-level sets, adopted in this work is also described. The convenience of applying Hierarchical HLFPN to the modeling of α-sets inference is discussed using a basic inference pattern with fuzzy rules.148859871Looney, C., (1988) IEEE Trans Syst Man Cybernetics, 18, pp. 178-183Bugarin, A., Barro, S., (1994) IEEE Trans Fuzzy Syst, 2, pp. 135-150Cao, T., Sanderson, A., (1993) Application and Theory of Petri Nets, pp. 126-145. , Marsan, M. A., Ed., LNCS, Springer-Verlag: Berlin/New YorkChun, M., Bien, Z., (1993) IEICE Trans Fund, E76-AChen, S., Ke, J.S., Chang, J., (1990) IEEE Trans Know and Data Engin, pp. 311-319Pedrycz, W., Gomide, F., (1994) IEEE Trans Fuzzy Syst, 2, pp. 295-301Cardoso, J., Valette, R., Dubois, D., Advances in Petri nets (1990) LNCS, 483, pp. 64-78. , Rosenberg, G., Ed.Springer-Verlag: Berlin/New YorkValette, R., Cardoso, J., Dubois, D., Monitoring manufacturing systems by means of Petri nets with imprecise markings (1989) IEEE International Symposium on Intelligent Control, pp. 233-238. , Albany, N.Y. USA, Sept. 25-26Scarpelli, H., Gomide, F., Fuzzy reasoning and high level fuzzy Petri nets (1993) First European Congress on Fuzzy and Intelligent Technologies, pp. 600-605. , Aachen, Germany, September 7-10Scarpelli, H., Gomide, F., Pedrycz, W., (1996) Intl J Uncertainty, Fuzziness KB Syst, 4, p. 1Scarpelli, H., Gomide, F., Fuzzy reasoning and fuzzy Petri nets (1993) Fifth IFSA World Congress, pp. 1326-1329. , Seoul, Korea, JulyScarpelli, H., Gomide, F., (1993) J Intell Fuzzy Syst, 1, p. 3Scarpelli, H., Gomide, F., Yager, R., (1996) IEEE Trans Fuzzy Syst, 4, p. 3Scarpelli, H., (1995) Proceedings of the 6th IFSA World Congress, pp. 345-348. , São Paulo, Brazil, JulyKeller, J., Yager, R., Tahani, H., (1992) Fuzzy Sets Syst, 45, pp. 1-12Scarpelli, H., Gomide, F., (1996) Fuzzy Modeling-paradigms and Practice, pp. 71-88. , Pedrycz, W., Ed.Kluwer Academic: Dordrecht/Norwell, MAScarpelli, H., Gomide, F., Yager, R., High level fuzzy Petri nets and inference based on α-level sets (1997) Seventh IFSA World Congress, pp. 282-294. , Prague, JulyUehara, K., Fujise, M., (1993) IEEE Trans Fuzzy Syst, 1, pp. 111-124Camargo, H., (1998) Hierarchical Constructs and High Level Fuzzy Petri Nets, , IPMU 98, Paris, France, July 4-9, (accepted for publication)Camargo, H., The Design of Knowledge Bases through Hierarchical Fuzzy Petri Nets, , submitted for publicationYager, R., Filev, D., (1994) Essentials of Fuzzy Modeling and Control, , Wiley: New YorkPedrycz, W., (1993) Fuzzy Control and Fuzzy Systems, , Wiley: New YorkGenrich, H.J., Predicate/transition nets (1986) Petri Nets: Central Models and their Properties, Lecture Notes in Computer Science, 254, pp. 207-247. , Brauer, W.Reisig, W.Rozenberg, G., Eds.Springer-VerlagJensen, K., Coloured Petri nets (1986) Petri Nets: Central Models and their Properties, Lecture Notes in Computer Science, 254, pp. 248-299. , Brauer, W.Reisig, W.Rozenberg, G., Eds.Springer-VerlagZadeh, L.A., (1979) Machine Intelligence, 9, pp. 149-194. , Hayes, J. E.Michie, D.Mikulich, L. I., Eds.Ellis Horwood: ChichesterYager, R., (1980) Internt J Man-machine Studies, 13, pp. 323-338Nafarieh, A., Keller, J., (1991) Fuzzy Sets Syst, 41, pp. 17-37Klir, G., Yuan, B., (1995) Fuzzy Sets and Fuzzy Logic, Theory and Applications, , Prentice Hall: New YorkJensen, K., Coulored Petri nets: A high level language for systems design and analysis (1990) Advances in Petri Nets, Lecture Notes in Computer Science, 483, pp. 342-415. , Rozenberg, G., Ed.Springer-Verlag: Berlin/New Yor
New Neurofuzzy Training Procedure Based On Participatory Learning Paradigm
No full textIn this paper we introduce a new approach to train neurofuzzy networks using the participatory learning concept. The participatory learning paradigm tends to emulate the human learning mechanism where an acceptance mechanism determines which observation is used for learning based upon their compatibility with the current beliefs. The performance of the proposed learning scheme is illustrated by an example involving a nonlinear system modeling problem: the thermal modeling of power transformers. Comparisons with other methods reported in the literature and between two dual network structures are also included. The experimental results show the effectiveness of participatory learning in neurofuzzy networks training. © 2007 IEEE.Lin, C.C., Lee, C.S.J., Neural network based fuzzy logic control and decision system (1991) IEEE Trans. on Systems Man and Cybernetics, 40 (12), pp. 1320-1336Jang, R., ANFIS: Adaptive network based fuzzy inference system (1993) IEEE Trans. on Systems Man and Cybernetics, 23 (3), pp. 665-685Lin, C.T., Lee, C.S., (1996) Neural Fuzzy Systems, , Prentice Hall, NJ, USAKosko, B., (1991) Neural Networks and fuzzy Systems: A Dynamical System Approach to Machine Intelligence, , Prentice HallHorikawa, S., On Fuzzy modeling using fuzzy neural networks with the back-propagation algorithm (1992) IEEE Trans. On Neural Networks, 3 (4)Figueiredo, M., Ballini, R., Soares, S., Andrade, M., Gomide, F., Learning Algorithms for a class of Neurofuzzy Network and Application (2004) IEEE Trans. on System, Man and Cybernetics ?Part C, 34 (3), pp. 293-301Silva, L., Gomide, F., Yager, R.R., Participatory Learning in Fuzzy Clustering (2005) Proc. 14th IEEE International Conferece on Fuzzy Systems, pp. 857-861. , Reno, USA, MayYager, R.R., A model of Participatory Learning (1990) IEEE Trans. on System, Man and Cybernetics, 20 (5), pp. 1229-1234Yager, R.R., Participatory Learning: A Paradigm for More Human Like Learning (2004) Proc. 2004 IEEE International Conference on Fuzzy Systems, 1, pp. 79-84. , JulyLima, E., Gomide, F., Ballini, R., Participatory Evolving Fuzzy Modeling (2006) Proc. 2006 International Symposium on Evolving Fuzzy Systems, pp. 36-41. , Lake district, UK, SeptPedrycz, W., Gomide, F., (1998) An Introduction of Fuzzy Sets: Analisys and Design, , Mrr Press, Cambridge, MA, USAM. Hell, P. Costa Jr., F. Gomide, Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers, IEEE Trans. on Power Delivery, 22, no. 2, 2007. In PressHell, M., Ballini, R., Costa Jr., P., Gomide, F., Training Neurofuzzy Networks with Participatory Learning Caminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) Journal of Advanced Computational Intelligence and Intelligent Informatics, 3 (3), pp. 151-156. , Jun
Equality Index And Learning In Recurrent Fuzzy Neural Networks
No full textA novel learning algorithm for recurrent neurofuzzy networks is introduced in this paper. The core of the learning algorithm uses equality index as the performance measure to be optimized. Equality index is especially important because its properties reflect the fuzzy set-based structure of the neural network and nature of learning. Equality indexes are strongly tied with the properties of the fuzzy set theory and logic-based techniques. The neural network recurrent topology is built with fuzzy neuron units and performs neural processing consistent with fuzzy system methodology. Therefore neural processing and learning are fully embodied within fuzzy set theory. The performance recurrent neurofuzzy network is verified via examples of nonlinear systems modeling. Computational experiments show that the recurrent fuzzy neural models developed are simpler and that learning is faster than both, static neural and neural fuzzy networks and alternative recurrent fuzzy neural networks.1155160Ku, C.C., Lee, K.L., Diagonal recurrent neural networks for dynamic systems control (1995) IEEE Trans. on Neural Networks, 6, pp. 144-156. , JanuaryWilliams, R.J., Zipser, D., A learning algorithm for continually running fully recurrent neural networks (1989) Neural Computation, (1), pp. 270-280Lee, C.H., Teng, C.C., Identification and control of dynamic systems using recurrent fuzzy neural networks (2000) IEEE Trans. on Fuzzy Systems, 8 (4), pp. 3493-366. , AugustBuckley, A., Hayashi, Y., Fuzzy neural networks: A survey (1994) Fuzzy Sets and Systems, 66, pp. 41-49Lin, C.T., Lee, C.S., (1996) Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, , Prentice Hall, Upper Saddle River, N.JNürnberger, A., Radetzky, A., Kruse, R., Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems (2001) Neurocomputing, 36, pp. 123-147Nürnberger, A., A hierarchical recurrent neuro-fuzzy system (2001) Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 1407-1412. , IEEEPedrycz, W., Neurocomputations in relational systems (1991) IEEE Trans. Pattern Analysis and Machine Intelligence, 13 (3), pp. 289-297. , MarchPedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , MIT Press, Cambridge, MACaminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157Ballini, R., Scares, S., Gomide, F., A recurrent neurofuzzy network structure and learning procedure (2001) Proc. of the 10th IEEE International Conference on Fuzzy Systems - FUZZ-IEEE'2001, 3Pedrycz, W., Rocha, A., Fuzzy-set based models of neuron and knowledge-based networks (1998) IEEE Trans. on Fuzzy Systems, 4 (1), pp. 254-266Nozaki, K., Ishibuchi, H., Tanaka, H., Trainable fuzzy classification systems based on fuzzy if-then rules (1994) Proc. of the 3th IEEE International Conference on Fuzzy Systems, pp. 408-502Oliveira, M., Figueiredo, M., Gomide, F., A neurofuzzy approach for autonomous control (1994) Proc. of the 3th IEEE International Conference on Fuzzy Systems, Neural Nets and Soft Computing, pp. 597-598Rumelhart, D., Hinten, G.E., Williams, R.J., Learning representations by back-propagating errors (1986) Nature (London), 323, pp. 533-536Barto, A.G., Jordan, M.I., Gradient following without back-propagation in layered networks (1987) Proc. of the IEEE 1st International Conference on Neural Networks, 2, pp. 629-636. , San DiegoNarendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Trans. on Neural Networks, 1 (1)Wang, L.X., (1994) Adaptive Fuzzy Systems and Control, , Prentice-Hal
Multiagent Coevolutionary Genetic Fuzzy System To Develop Bidding Strategies In Electricity Markets: Computational Economics To Assess Mechanism Design
No full textThis paper suggests a genetic fuzzy system approach to develop bidding strategies for agents in online auction environments. Assessing efficient bidding strategies is a key to evaluate auction models and verify if the underlying mechanism design achieves its intended goals. Due to its relevance in current energy markets worldwide, we use day-ahead electricity auctions as an experimental and application instance of the approach developed in this paper. Successful fuzzy bidding strategies have been developed by genetic fuzzy systems using coevolutionary algorithms. In this paper we address a coevolutionary fuzzy system algorithm and present recent results concerning bidding strategies behavior. Coevolutionary approaches developed by coevolutionary agents interact through their fuzzy bidding strategies in a multiagent environment and allow realistic and transparent representations of agents behavior in auction-based markets. They also improve market representation and evaluation mechanisms. In particular, we study how the coevolutionary fuzzy bidding strategies perform against each other during hourly electric energy auctions. Experimental results show that coevolutionary agents may enhance their profits at the cost of increasing system hourly price paid by demand. © Springer-Verlag 2009.21-25371Tesfatsion, L., Judd, K.L., (2006) Handbook of computational economics: Agent-based computational economics volume 2 of Handbooks in Economics, , North HollandHerrera, F., Genetic fuzzy systems: Taxonomy, current research trends and prospects (2008) Evol Intell, 1, pp. 27-46Walter, I., Gomide, F., Design of coordination strategies in multiagent systems via genetic fuzzy systems (2006) Soft Comput, 10 (10), pp. 903-915. , Special Issue: New Trends in the Design of Fuzzy SystemsWalter, I., Gomide, F., Genetic fuzzy systems to evolve coordination strategies in multiagent systems (2007) Int J Intell Syst, 22 (9), pp. 971-991. , Special Issue on Genetic Fuzzy SystemsWalter, I., Gomide, F., Coevolutionary fuzzy multiagent bidding strategies in competitive electricity markets (2008) 3rd International workshop on genetic and evolving fuzzy systems (GEFS 08), pp. 53-58. , Witten-Bommerholz, Germany, IEEEWalter, I., Gomide, F., Coevolutionary genetic fuzzy system to assess multiagent bidding strategies in electricity markets (2009) Proceedings of the joint 2009 international fuzzy systems association world congress and 2009 european society of fuzzy logic and technology conference, pp. 1114-1119. , Lisbon, PortugalSilva, C., Wollenberg, B.F., Zheng, C.Z., Application of mechanism design to electric power markets (2001) IEEE Trans Power Syst, 16 (4), pp. 862-869Green, R., Competition in generation: The economic foundations (2000) Proceedings of the IEEE, 88 (2), pp. 128-139David, A.K., Wen, F.S., Strategic bidding in competitive electricity markets: A literature survey (2000) IEEE PES 2000 summer power meeting, 4, pp. 2168-2173. , IEEE Power Engineering Society, IEEE, SeattleVisudhipan, P., Ilic, M., Dynamic games-based modeling of electricity markets (1999) IEEE power engineering society winter meeting, 1, pp. 274-281. , IEEE, New YorkMonclar, F-R., Quatrain, R., Simulation of electricity markets: A multi-agent approach (2001) International conference on intelligent system application to power systems, pp. 207-212. , IEEE Power Engineering Society, Budapest, HungaryRichter, C.W., Sheblé, G.B., Building fuzzy bidding strategies for the competitive generator (1997) North american power symposium, , Laramie, Wyoming, USAWidjaja, M., Sugianto, L.F., Morrison, R.E., Fuzzy model of generator bidding system in competitive electricity markets (2001) 10th IEEE international conference on fuzzy systems, 3, pp. 1396-1399. , IEEE, Melbourne, AustraliaRichter Jr., C.W., Sheblé, G.B., Genetic algorithm evolution of utility bidding strategies for the competitive marketplace (1998) IEEE Trans Power Syst, 13 (1), pp. 256-261Richter Jr., C.W., Sheblé, G.B., Ashlock, D., Comprehensive bidding strategies with genetic programming/finite state automata (1999) IEEE Trans Power Syst, 14 (4), pp. 1207-1212Xiong, G., Hashiyama, T., Okuma, S., An evolutionary computation for supplier bidding strategy in electricity auction market (2002) IEEE power engineering society transmission and distribution conference, 1, pp. 83-88. , IEEE, Yokohama, JapanBagnall, A.J., A multi-adaptive agent model of generator bidding in the UK market in electricity (2000) Genetic and evolutionary computation conference GECCO 2000, pp. 605-612. , Morgan Kaufmann, Los AltosTesauro, G., Pricing in agent economies using neural networks and multi-agent Q-learning (2000) Sequence learning volume 1828 of Lecture Notes in Artificial Intelligence, pp. 288-307. , Sun R, Giles CL, (eds), Springer, BerlinHu, J., Wellman, M.P., Nash Q-learning for general-sum stochastic games (2003) J Mach Learn Res, 4, pp. 1039-1069Singh, H., Introduction to game theory and its application in electric markets (1999) IEEE Comput Appl Power, 12 (4), pp. 18-22Axelrod, R., (1984) The evolution of cooperation, , Basic Books, New YorkSandholm, T.W., Crites, R.H., Multiagent reinforcement learning in the iterated prisoner's dilemma (1996) Biosystems, 37 (1-2), pp. 147-166Hingston, P., Kendall, G., Learning versus evolutionin iterated prisoner's dilemma (2004) IEEE congress on evolutionary computation (CEC2004), 1, pp. 364-372Borges, P.S.S., Pacheco, R.C.S., Barcia, R.M., Khator, S.K., A fuzzy approach to the prisoner's dilemma (1997) Biosystems, 41 (2), pp. 127-137Amaral, W., Gomide, F., Granular computing: At the junction of rough sets and fuzzy sets (2008) Studies in fuzziness and soft computing, pp. 121-130. , volume 224, chapter A coevolutionary approach to solve fuzzy games. Springer, BerlinAmaral, W., Gomide, F., Theoretical advances and applications of fuzzy logic and soft computing (2007) Advances in soft computing, volume 42, chapter an algorithm to solve two-person non-zero sum fuzzy games, pp. 296-302. , Springer, BerlinChen, H., Wong, K., Nguyen, D., Chung, C., Analyzing oligopolistic electricity market using coevolutionary computation (2006) IEEE Trans Power Syst, 21 (1), pp. 143-152Chen, H., Wong, K., Wang, X., Chung, C., A coevolutionary approach to modeling oligopolistic electricity markets (2005) IEEEZhang, S.X., Chung, C.Y., Wong, K.P., Chen, H., Analyzing twosettlement electricity market equilibrium by coevolutionary computation approach (2009) IEEE Trans Power Syst, 23 (3), pp. 1155-1164Cau, T.D.H., Anderson, E., A co-evolutionary approach to modelling the behaviour of participants in competitive electricity markets (2002) 2002 IEEE power engineering society summer meeting, 3, pp. 1534-1540. , ChicagoAnderson, E.J., Cau, T.D.H., Modeling implicit collusion using coevolution (2009) Oper Res, 57 (2), pp. 439-455Son, Y.S., Baldick, R., Hybrid coevolutionary programming for nash equilibrium search in games with local optima (2004) IEEE Trans Evol Comput, 8 (4), pp. 305-315Phelps, S.G., Parsons, S., McBurney, P., Sklar, E., Co-evolution of auction mechanims and trading strategies: Towards a novel approach to microeconomic design (2002) Proceedings of the 2nd workshop on evolutionary computation and multi-agent systems, , New YorkPhelps, S.G., (2007) Evolutionary mechanism design, , PhD thesis, Univeristy of Liverpool, UKNicolaisen, J., Petrov, V., Tesfatsion, L., Market power and efficiency in a computational electricity market with discriminatory double-auction pricing (2001) IEEE Trans Evol Comput, 5 (5), pp. 504-523de la Cal Marín, E.A., Sánchez Ramos, L., Optimizing supply strategies in the spanish market (2003) Lecture Notes on Computer Science, 2687, pp. 353-360de la Cal Marín, E.A., Sánchez Ramos, L., Supply estimation using coevolutionary genetic algorithms in the spanish market (2004) Appl Intell, 21 (1), pp. 7-24de la Cal Marín, E.A., Suárez Fernández, M.D.R., Application of an optimizing supply strategies model in the spanish electrical market (2003) International congress on evolutionary methods for design, optimization and control with applications to industrial problems (EURGOEN 2003), , Barcelona, Spain, CIMNEBajpai, P., Singh, S.N., Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market (2007) IEEE Trans Power Syst, 22 (4), pp. 2152-2160Ma, Y., Jiang, C., Hou, Z., Wang, C., The formulation of the optimal strategies for the electricity producers based on the particle swarm optimization algorithm (2006) IEEE Trans Power Syst, 21 (4), pp. 1663-1671Yucekaya, A.D., Valenzuela, J., Dozier, G., Strategic bidding in electricity markets using particle swarm optimization (2009) Electr Power Syst Res, 79 (2), pp. 335-345Harp, S.A., Brignone, S., Wollenberg, B.F., Samad, T., SEPIA: A simulator for electric power industry agents (2000) IEEE Control Syst Mag, 20 (4), pp. 53-69Praça, I., Ramos, C., Vale, Z., Cordeiro, M., A new agentbased framework for the simulation of electricity markets (2003) Proceedings of the IEEE/WIC international conference on intelligent agents (IAT'03), pp. 1931-1938. , IEEE, Halifax, CanadaWalter, I., Gomide, F., Electricity market simulation: Multiagent system approach (2008) 23rd Annual ACM symposium on applied computing, 1, pp. 34-38. , Fortaleza, CE, BrazilAl-Agtash, S., Yamin, H.Y., Optimal supply curve bidding using Benders decomposition in competitive electricity markets (2004) Electr Power Syst Res, 71, pp. 245-255Cordon, O., Gomide, F., Herrera, F., Hoffman, F., Magdalena, L., Ten years of genetic fuzzy systems: Current framework and new trends (2004) Fuzzy Sets and Syst, 141 (1), pp. 5-31. , Special Issue on Genetic Fuzzy Systems: New DevelopmentsPedrycz, W., Gomide, F., (2007) Fuzzy systems engineering: Toward human-centric computing, , Wiley-IEEE, HobokenHillis, W.D., Co-evolving parasites improve simulated evolution as an optimization procedure (1990) Physica D, 42, pp. 228-234Potter, M.A., de Jong, K.A., Cooperative coevolution: An architecture for evolving coadapted subcomponents (2000) Evol Comput, 8 (1), pp. 1-29Cordón, O., Herrera, F., Hoffman, F., Magdalena, L., (2001) Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases, volume 19 of advances in fuzzy systems: Applications and theory, , World Scientific, SingaporeCordón, O., Herrera, F., Villar, P., Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base (2001) IEEE Trans Fuzzy Syst, 9 (4), pp. 667-674Herrera, F., Lozano, M., Verdegay, J.L., Fuzzy connectives based crossover operators to model genetic algorithms population diversity (1997) Fuzzy Sets Syst, 92 (1), pp. 21-30Vidal, J.M., Learning in multiagent systems: An introduction from a 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trough the evolution of its knowledge base (1997) Int J Approx Reason, 16 (3-4), pp. 335-358Michalewicz, Z., (1996) Genetic algorithms? data structures = evolution programs, , Springer, Vienn
A Fast Learning Algorithm For Uninorm-based Fuzzy Neural Networks
No full textThis paper suggests a fast learning algorithm for weighted uninorm-based neural networks. Fuzzy neural networks are models capable to approximate functions with high accuracy and to generate transparent models through extraction of linguistic information from the resulting topology. A fuzzy neural network model based on weighted uninorms has been developed recently. It was shown that this model approximates any continuous real function on a compact subset. In this paper we introduce a fast learning algorithm for this class of fuzzy neural networks based on ideas from extreme learning machine. The algorithm is detailed and computational experiments reported to illustrate the accuracy and time efficiency of the learning approach. The results show that neural fuzzy model is accurate and learning speed is as good as or faster than alternative neural network models. © 2012 IEEE.Minist. Commun. Inf. Technol. Republic AzerbaijanPedrycz, W., Fuzzy Neural Networks and Neurocomputations (1993) Fuzzy Sets and Systems, 56 (1), pp. 1-28. , MAY 25Caminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy sets based neural networks: Structure, learning and applications (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157Gomide, F., Pedrycz, W., (2007) Fuzzy Systems Engineering: Toward Human-Centric Computing, , NJ, USA: Wiley InterscienceBallini, R., Gomide, F., Learning in recurrent, hybrid neurofuzzy networks (2002) IEEE International Conference on Fuzzy Systems, pp. 785-791Hell, M., Costa, P., Gomide, F., Participatory learning in power transformers thermal modeling (2008) IEEE Transactions on Power Delivery, 23 (4), pp. 2058-2067. , OctGobi, A.E., Pedrycz, D., Logic minimization as an efficient means of fuzzy structure discovery (2008) IEEE Transactions on Fuzzy Systems, 16 (3), pp. 553-566. , JUNPedrycz, W., Logic-based fuzzy neurocomputing with unineurons (2006) IEEE Transactions on Fuzzy Systems, 14 (6), pp. 860-873. , DECPedrycz, W., Hirota, K., Uninorm-based logic neurons as adaptive and interpretable processing constructs (2007) Soft Computing, 11 (1), pp. 41-52. , JANHell, M., Gomide, F., Ballini, R., Costa, P., Uninetworks in time series forecasting (2009) NAFIPS 2009. Annual Meeting of the North American Fuzzy Information Processing Society, 2009, pp. 1-6. , juneLemos, A., Caminhas, W., Gomide, F., New uninorm-based neuron model and fuzzy neural networks (2010) 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1-6Lemos, A., Kreinovich, V., Caminhas, W., Gomide, F., Universal approximation with uninorm-based fuzzy neural networks (2011) 2011 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1-6. , marchYager, R., Rybalov, A., Uninorm aggregation operators (1996) Fuzzy Sets and Systems, 80 (1), pp. 111-120. , MAY 27Calvo, T., Baets, B.D., Fodor, J., The functional equations of frank and alsina for uninorms and nullnorms (2001) Fuzzy Sets and Systems, 120 (3), pp. 385-394Herrera, F., Lozano, M., Verdegay, J.L., Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis (1998) Artif. Intell. Rev., 12 (4), pp. 265-319Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., Extreme learning machine: A new learning scheme of feedforward neural networks (2004) 2004 IEEE International Joint Conference on Neural Networks (IJCNN), 2, pp. 985-990. , july vol.2Huang, G.-B., Chen, L., Siew, C.-K., Universal approximation using incremental constructive feedforward networks with random hidden nodes (2006) Neural Networks, IEEE Transactions on, 17 (4), pp. 879-892. , julyYager, R., Uninorms in fuzzy systems modeling (2001) Fuzzy Sets and Systems, 122 (1), pp. 167-175. , AUG 16Huang, G.-B., Siew, C.-K., Extreme learning machine with randomly assigned rbf kernels (2005) International Journal of Information Technology, 11 (1), pp. 16-24Montesino-Pouzols, F., Lendasse, A., Evolving fuzzy optimally pruned extreme learning machine for regression problems (2010) Evolving Systems, 1 (1), pp. 43-58. , AugustBartlett, P., The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network (1998) IEEE Transactions on Information Theory, 44 (2), pp. 525-536. , marSerre, D., (2002) Matrices: Theory and Applications, , New York, US: Springer- VerlagBox, G.E.P., Jenkins, G., (1990) Time Series Analysis, Forecasting and Control, , Holden-Day, IncorporatedRiedmiller, M., Braun, H., A direct adaptive method for faster backpropagation learning: The rprop algorithm (1993) IEEE International Conference on Neural Networks, 1993, 1, pp. 586-591Jang, J., ANFIS - Adaptive-Network-Based Fuzzy Inference System (1993) IEEE Transactions on Systems Man and Cybernetics, 23 (3
Combining Forecasts For Natural Streamflow Prediction
No full textThis paper proposes an approach to combine forecasts generated by a set of individual forecasting models in a simple and effective way. In principle, combination can be done using appropriate aggregation operators, but here we use a neural network trained with the gradient algorithm. The aim is to combine the forecasts generated by the different forecasting models as an attempt to capture the contributions of the most Important prediction features of each individual model at each prediction step. The approach is used for streamflow time series prediction choosing, as individual forecasting models, periodic autoregressive moving average model (PARMA), and two fuzzy clustering-based forecasting models. Experimental results with actual streamflow data show that the combination approach performs better than each of the individual forecasting models and yet, when compared to a fuzzy neural network (FNN) evaluation, the suggested combination model shows lower prediction errors.1390394Box, G., Jenkins, G., Reinsel, G.C., (1994) Time Series Analysis, Forecasting and Control, 3rd Ed., , Oakland, California: Holden DayWeigend, A.S., Gershenfeld, N., (1993) Time Series Prediction: Prediction de Future and Understanding the Past, , Perseus PublishingPedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , Cambridge, MA: MIT PressBallini, R., Figueiredo, M., Soares, S., Andrade, M., Gomide, F., A seasonal streamflow forecasting model using neurofuzzy network (2000) Information, Uncertainty and Fusion, pp. 257-276. , Kluwer Academic Publishers: B. Bouchon- Meunier and R. R. Yager and L. Zadeh, EdsSee, L., Openshaw, S., A hybrid multi-model approach to river level forecasting (2000) Hydrology Science Journal, (45), pp. 523-536Chang, F., Chen, Y., A counterpropagation fuzzy neural network modeling approach to real time streamflow prediction (2001) Journal of Hydrology, (245), pp. 153-164Geva, A., Non-stationary time series prediction using fuzzy clustering (1999) Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society, pp. 413-417Armstrong, J., Combining forecasts: The end of the beginning or the beginning of the end? (1989) International Journal of Forecasting, 5 (4), pp. 585-588Makridakis, S., Why combining works? (1989) International Journal of Forecasting, 5 (4), pp. 601-603Clemen, R., Combining forecasts: A review and annotated bibliography (1989) International Journal of Forecasting, 5 (4), pp. 559-583Sharkey, A., (1999) Combining Artificial Neural Nets: Ensemble and Modular Multi-net Systems, , SpringerPetridis, V., Kehagias, A., (1998) Predictive Modular Neural Networks, , Kluwer Academic PublishersVecchia, A., Maximum likelihood estimation for periodic autoregressive moving average models (1985) Technometrics, 27 (4), pp. 375-384Magalhães, M., Ballini, R., Gomide, F., Predictive fuzzy clustering model for natural streamflow forecasting (2004) Proceedings of FUZZY-IEEE'04, , Budapest, Hungary, July (submitted)Bezdek, J., (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, , Plenum PressFigueiredo, M., Gomide, F., Adaptive neuro fuzzy modelling (1997) Proceedings of FUZZ-IEEE'97, pp. 1567-1572Anderson, P.L., Vecchia, A.V., Asymptotic results for periodic autoregressive moving average processes (1993) Journal Time Series Anal, 1, pp. 1-18Schwarz, G., Estimating the dimension of a model (1978) Ann. Statist., 6 (2), pp. 461-468Haykin, S., (1994) Neural Networks - A Comprehensive Foundation, , New York: IEEE PressFigueiredo, M., Ballini, R., Soares, S., Andrade, M., Gomide, F., Learning algorithms for a class of neurofuzzy network and application (2004) IEEE Transactions on Man System and Cybernetics, Part C, , in pres
Heuristic Learning In Recurrent Neural Fuzzy Networks
No full textA novel recurrent neural fuzzy network is proposed in this paper. The network model is composed by two structures: a fuzzy system and a neural network. The fuzzy system contains fuzzy neurons modeled with the aid of logic and and or operations processed via t-norms and s-norms. The neural network is composed by nonlinear elements placed in series with the previous logical elements. The network model implicitly encodes a set of if-then rules and its recurrent multilayered structure performs fuzzy inference. The topology induces a clear relationship between the network structure and an associated fuzzy rule-based system. In particular we explore this structure with an heuristic learning algorithm based on associative reinforcement learning and gradient search. These learning algorithms are associated to the fuzzy system and neural network, respectively. That is, output layer weights are adjusted via an error gradient method whereas a reward and punishment scheme updates the hidden layer weights. The recurrent fuzzy neural network is particularly suitable to model nonlinear dynamic systems and to learn sequences. Computational experiments with system identification problems show that the fuzzy neural models learned are simpler and that learning is faster than its counterparts.1302/04/156374Narendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Trans. on Neural Networks, 1 (1), p. 4Atiya, A.F., El-Shoura, S.M., Shaheen, S.I., El-Sherif, M.S., A comparison between neural-network forecasting techniques - Case study: River flow forecasting (1999) IEEE Trans. on Neural Networks, 10 (2), pp. 402-409. , MarchGalicki, M., Leistritz, L., Witte, H., Learning continuous trajectories in recurrent neural networks with time-dependent weights (1999) IEEE Trans. on Neural Networks, 10 (4), pp. 741-756. , JulySantos, E.P., Von Zuben, F.J., Efficient second-order learning algorithms for discrete-time recurrent neural networks (1999) Recurrent Neural Networks: Design and Applications, pp. 47-75. , L.R. Medsker and L.C. Jain, eds, CRC PressFunahashi, K.I., Nakamura, Y., Approximation of dynamical systems by continuous time recurrent neural networks (1993) Neural Networks, 6, pp. 801-806Jin, L., Pikiforuk, P.N., Gupta, M.M., Approximation capability of feedforward and recurrent neural networks (1995) Intelligent Control Systems: Concepts and Applications, , M.M. Gupta and N.K. Sinha, eds, IEEE PressWilliams, R.J., Zipser, D., A learning algorithm for continually running fully recurrent neural networks (1989) Neural Computing, (1), pp. 270-280Williams, R.J., Peng, T.M., An efficient gradient-based algorithm for on-line training of recurrent network trajectories (1990) Neural Computing, (2), pp. 490-501Werbos, P.J., Backpropagation through time: What it does and how to do it (1990) Proc. IEEE, 10 (78), pp. 1550-1560Lee, C.H., Teng, C.C., Identification and control of dynamic systems using recurrente fuzzy neural networks (2000) IEEE Trans. on Fuzzy Systems, 8 (4), pp. 3493-366. , AugustBuckley, A., Hayashi, Y., Fuzzy neural networks: A survey (1994) Fuzzy Sets and Systems, 66, pp. 41-49Lin, C.T., Lee, C.S., (1996) Neural Fuzzy Systems: A Neuro-fuzzy Synergism to Intelligent Systems, , Prentice Hall, Upper Saddle River, N.JLin, F.J., Hwang, W.J., Wai, R.J., A supervisory fuzzy neural network control system for tracking periodic inputs (1999) IEEE Trans. on Fuzzy Systems, 7, pp. 41-52. , FebruaryChen, Y.C., Teng, C.C., A model reference control structure using a fuzzy neural network (1995) Fuzzy Sets and Systems, 73, pp. 291-312Wang, L.X., (1997) A Course in Fuzzy Systems and Control, , Prentice-HallCastro, J., Fuzzy logic controllers are universal approximators (1995) IEEE Trans. on Systems, Man, and Cybernetics, 25 (4), pp. 629-635Wang, L.X., (1994) Adaptive Fuzzy Systems and Control, , Prentice-HallCastro, J., Delgado, M., Fuzzy systems with defuzzification are universal approximators (1996) IEEE Trans. on Systems, Man, and Cybernetics, 26 (1), pp. 149-152Blanco, A., Delgado, M., Pegalajar, M.C., Identification of fuzzy dynamic systems using Max-Min recurrent neural networks (2001) Fuzzy Sets and Systems, 122 (3), pp. 451-467. , SeptemberBallini, R., Scares, S., Gomide, F., A recurrent neurofuzzy network structure and learning procedure (2001) 10th IEEE International Conference on Fuzzy Systems- FUZZ-IEEE 2001, 3Caminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157Pedrycz, W., Rocha, A., Fuzzy-set based models of neuron and knowledge-based networks (1998) IEEE Transactions on Fuzzy Systems, 4 (1), pp. 254-266Pedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , MIT Press, Cambridge, MABarto, A.G., Jordan, M.I., Gradient following without back-propagation in layered networks (1987) Proceedings of the IEEE First International Conference on Neural Networks, 2, pp. 629-636. , San DiegoNozaki, K., Ishibuchi, H., Tanaka, H., Trainable fuzzy classification systems based on fuzzy if-then rules (1994) Proc. of the Third IEEE International Conference on Fuzzy Systems, pp. 408-502Oliveira, M., Figueiredo, M., Gomide, F., A neurofuzzy approach for autonomous control (1994) Proc. of the Third IEEE International Conference on Fuzzy Systems, Neural Nets and Soft Computing, pp. 597-598Rumelhart, D., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors (1996) Nature (London), 323, pp. 533-536Barto, A.G., Anadan, P., Pattern recognizing stochastic learning automata (1985) IEEE Trans. on Systems, Man, and Cybernetics, 15, pp. 360-375Lee, C.H., Teng, C.C., A model reference control structure using a fuzzy neural network (1995) Fuzzy Sets and Systems, 73, pp. 291-31
Participatory Learning In The Neurofuzzy Short-term Load Forecasting
No full textThis paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid neurofuzzy network to forecast 24-h daily energy consumption series of an electrical operation unit located at the Southeast region of Brazil. Experimental results show that the neurofuzzy approach with participatory learning requires less computational effort, is more robust, and more efficient than alternative neural methods. The approach is particularly efficient when training data reflects anomalous load conditions or contains spurious measurements. Comparisons with alternative approaches suggested in the literature are also included to show the effectiveness of participatory learning.176182Infield, D.G., Hill, D.C., Optimal smoothing for trend removal in short term electricity demand forecasting (1998) IEEE Trans. Power Systems, 13 (3), pp. 1115-1120. , AugPapalexopoulos, A.D., Hesterberg, T.C., A regression-based approach to short-term load forecasting (1990) IEEE Trans. Power Systems, 5 (4), pp. 1535-1550. , NovRahman, S., Hazim, O., A generalized knowledge-based short term load-forecasting technique (1993) IEEE Trans. Power Systems, 8 (2), pp. 508-514. , MayHuang, S.J., Shih, K.R., Short-term load forecasting via arma model identification including non-gaussian process considerations (2003) IEEE Trans. Power Systems, 18 (2), pp. 673-679. , MayBox, G.E.P., Jenkins, G.M., (1976) Time Series Analysis-Forecasting and Control, , Holden-Day, San Francisco, CA-USAIrisarri, G.D., Widergren, S.E., Yehsakul, P.D., On-line load forecasting for energy control center application (1982) IEEE Trans. on Power Apparatus and Systems, 101 (1), pp. 71-78. , JanHaykin, S., (1998) Neural Networks: A Comprehensive Foundation", 2. , Prentice Hall, NJ-USA, edHippert, H.S., Pedreira, C.E., Souza, R.C., Neural networks for short-term load forecasting: A review and evaluation (2001) IEEE Trans. Power Systems, 16 (1), pp. 44-55. , FebLing, S.H., Leung, F.H.F., Lam, H.K., Tam, P.K.S., Short-term electric load forecasting based on a neural fuzzy network (2003) IEEE Transactions on Industrial Electronics, 50 (6), pp. 1305-1316. , DecFan, S., Chen, L., Short-term load forecasting based on an adaptive hybrid method (2006) IEEE Trans. Power Systems, 21 (1), pp. 392-401. , FebArora, S., Taylor, J.W., Short-term forecasting of anomalous load using rule-based triple seasonal methods (2013) IEEE Transactions on Power Systems, 28 (3), pp. 3235-3242. , AugMao, H., Zeng, X.-J., Leng, G., Zhai, Y., Keane, J., Short-term load forecasting based on self-organizing fuzzy neural networks (2007) Proc. of IEEE International Conference on Fuzzy Systems, London-UK, Jully, pp. 1-6Yager, R.R., (1990) A Model of Participatory Learning, 20 (5), pp. 1229-1234. , IEEE Trans. on System, Man and CyberneticsYager, R.R., Participatory learning: A paradigm for more human like learning (2004) Proc. 2004 IEEE International Conference on Fuzzy Systems, 1, pp. 79-84. , JulySilva, L., Gomide, F., Yager, R.R., Participatory learning in fuzzy clustering (2005) Proc. 14th IEEE International Conferece on Fuzzy Systems, pp. 857-861. , Reno, USA, MayLima, E., Gomide, F., Ballini, R., Participatory evolving fuzzy modeling (2006) Proc. 2006 International Symposium on Evolving Fuzzy Systems, pp. 36-41. , Lake district, UK, SeptZadeh, L.A., Fuzzy sets (1965) Information Control, 8 (3), pp. 338-353. , JunePedrycz, W., Gomide, F., (2007) Fuzzy Systems Engineering: Toward Human-Centric Computing, , Wiley Interscience, Hoboken, NJ, USACaminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) J. Advanced Comput. Intell. Inform., 3 (3), pp. 151-156Gross, G., Galiana, F.D., Short-term load forecasting (1987) Proceedings of the IEEE, 75 (12), pp. 1558-1573. , DecKim, K.-H., Youn, H.-S., Kang, Y.-C., Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method (2000) IEEE Trans. Power Systems, 15 (2), pp. 559-565. , MayHell, M., Costa, P., Jr., Gomide, F., New neurofuzzy training procedure based on participatory learning paradigm (2007) Proc. of IEEE International Conference on Fuzzy Systems, pp. 1952-1957. , London-UK, JulBezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, , Plenum Press, New York, US
Hybrid Genetic Algorithms And Clustering
No full textThis paper introduces a hybrid genetic algorithm that uses fuzzy c-means clustering technique as a mechanism to reduce fitness evaluations and to preserve solution quality. Population clustering provides a means to evaluate only the representative individual of each cluster instead of the whole population. The remaining individuals are indirectly evaluated. The aim is to maintain reasonable population size and to obtain near-optimal solutions. This is an important issue especially in large-scale, complex optimization and decision-making problems.10091014Bezdek, J.C., (1987) Pattern Recognition with Fuzzy Objective Function Algorithms, , Plenum PressGoldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , Addison-Wesley Publishing Co.IncHanaki, Y., Hashiyama, T., Okuma, S., Accelerated evolutionary computation using fitness estimation (1999) IEEE Trans. SMC, 1, pp. 643-648Kado, K., Ross, P., Corne, D., A study of genetic algorithm hybrids for facility layout problems (1995) Proc. of ICGA, pp. 498-505Kim, H., Cho, S., An efficient genetic algorithm with less fitness evaluation by clustering (2001) IEEE Publication 0-7803-6657-3Mota Filho, F., Gonçalves, R., Gomide, F., Genetic algorithms, fuzzy clustering and discrete event systems: An application in scheduling (2005) Proc. of 1st Workshop on Genetic Fuzzy Systems, pp. 83-88. , Granada, SpainSalami, M., Hendtlass, T., A fitness estimation strategy for genetic algorithms (2002) Lecture Notes in Computer Science, 2358, pp. 502-513Unemi, T., A design of multi-field user interface for simulated breeding (1998) Proc. of the 3rd AFSS, pp. 489-49
Evolving Neo-fuzzy Neural Network With Adaptive Feature Selection
No full textThis paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature. © 2013 IEEE.341349Kasabov, N., Filev, D., Evolving intelligent systems: Methods, learning, & applications (2006) Proceedings of the International Symposium on Evolving Fuzzy Systems, pp. 8-18. , septLemos, A., Gomide, F., Caminhas, W., Multivariable gaussian evolving fuzzy modeling system IEEE Transactions on Fuzzy Systems, 1 (2011), pp. 91-104Lughofer, E., On-line incremental feature weighting in evolving fuzzy classifiers (2011) Fuzzy Sets Systems, 163 (1), pp. 1-23Li., Y., On incremental and robust subspace learning (2004) Pattern Recognition, 37, pp. 1509-1518Katakis, I., Tsoumakas, G., Vlahavas, I., Dynamic feature space and incremental feature selection for the classification of textual data streams (2006) Proc. Int. Workshop on Knowledge Discovery from Data Streams, pp. 107-116. , SpringerLemos, A., Caminhas, W., Gomide, F., Evolving fuzzy linear regression trees with feature selection (2001) Proc. of the IEEE Workshop on Evolving and Adaptive Intelligent Systems, 1, pp. 31-38Silva, A.M., Caminhas, W.M., Lemos, A.P., Gomide, F., Evolving neural fuzzy network with adaptive feature selection (2012) Machine Learning and Applications (ICMLA), pp. 440-445. , 11th International Conference on 2, dec 2012Silva, A.M., Caminhas, W.M., Lemos, A., Gomide, F., A fast learning algorithm for evolving neo-fuzzy neuron (2013) Applied Soft Computing, 0. , http://www.sciencedirect.com/science/article/pii/S1568494613001373, Online Available:Yamakawa, T., Uchino, E., Miki, T., Kusabagi, H., A neo fuzzy neuron and its applications to system identification and predictions to system behavior (1992) Proc. of the Int. Conf. on Fuzzy Logic and Neural Networks, 1, pp. 477-484Caminhas, W., Gomide, F., A fast learning algorithm for neofuzzy networks (2000) Proc. Information Processing and Management of Uncertainty in Knowledge Based Systems, 1 (1), pp. 1784-1790Bazaraa, M., Sherali, H., Shetty, C., (1993) Nonlinear Programming: Theory and Algorithms, , 3rd ed. John Wiley & SonsAllen, M., (1997) Understanding Regression Analysis, , 1st ed. Springer Ed. SpringerPotts, D., Sammut, C., Incremental learning of linear model trees (2004) Machine Learning, 61 (1), pp. 5-48Wang, D., Zeng, X., Keane, J., A structure evolving learning method for fuzzy systems Evolving Systems, 1 (2010), pp. 83-95Angelov, P., Filev, D., Simplets: A simplified method for learning evolving takagi-sugeno fuzzy models (2005) Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1068-1073. , FUZZ-IEEE ?05Lughofer, E., Angelov, P., Handling drifts and shifts in on-line data streams with evolving fuzzy systems (2011) Applied Soft Computing, 11 (2), pp. 2057-2068. , marKasabov, N., Song, Q., Denfis: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction (2002) IEEE Transactions on Fuzzy Systems, 10 (2), pp. 144-154Angelov, P., Filev, D., An approach to online identification of takagi-sugeno fuzzy models (2004) IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics, 34 (1), pp. 484-498Angelov, P., Zhou, X., Evolving fuzzy systems from data streams in real-Time (2006) Proc. of the Int. Symposium on Evolving Fuzzy Systems, pp. 29-35Jamsa, K., Klander, L., (1997) Jamsa?s C/C++ Programmer?s Bible: The Ultimate Guide to C/C++ Programming, , 2nd ed. PearsonMackey, M., Glass, L., Oscillation and chaos in physiological control systems (1977) Science, 197 (4300), pp. 287-289. , julyLeite, D., Ballini, R., Costa, P., Gomide, F., Evolving fuzzy granular modeling from nonstationary fuzzy data streams Evolving Systems, 3 (2012), pp. 65-7
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