1,721,005 research outputs found
Heuristics To Avoid Redundant Solutions On Population-based Multimodal Continuous Optimization
No full textIn population-based meta-heuristics, the generation and maintenance of diversity seem to be crucial to deal with multimodal continuous optimization. However, usually this crucial aspect is not an inherent feature of generally adopted meta-heuristics. In this paper, we propose to associate diversity maintenance with the detection and elimination of redundant candidate solutions in the search space, more specifically candidate solutions located at the same attraction basin of a local optimum. Two low computational cost heuristics are proposed to detect redundancy, in a pairwise comparison of candidate solutions and by extracting local features of the fitness landscape at runtime. Those heuristics are not tied to a specific class of algorithms, and are thus able to be incorporated into a broad range of population-based meta-heuristics, and even into multiple executions of non-population-based algorithms. In a set of experimental results, the two heuristics were implemented as an attached module of an already existing multipopulation meta-heuristics, and the results indicate that they operate properly, no matter the number and conformation of the attraction basins in multimodal optimization problems. © 2011 IEEE.23212328Czogala, E., Zimmermann, H., Hans-Jurgen, Decision making in uncertain environments (1986) European Journal of Operational Research, 23 (2), pp. 202-212. , Elsevier, FebruaryDeb, K., (2001) Multi-objective Optimization Using Evolutionary Algorithms, , WileyJin, Y.J., Branke, Evolutionary optimization in uncertain environments - A survey (2005) IEEE Trans. on Evolutionary Computation, 9 (3)Pasti, R., De Castro, L.N., Bio-inspired and gradient-based algorithms to train MLPs: The influence of diversity (2009) Information Sciences, 179, pp. 1441-1453Alba, E., (2005) Parallel Metaheuristics: A New Class of Algorithms, , WileyGlover, F., Kochenberger, G.A., (2003) Handbook of Metaheuristics, , SpringerBazaraa, M.M., Sherali, H.D.H.D., Shetty, C.M.C.M., (1993) Nonlinear Programming - Theory and Algorithms, , 2° edição, John Wiley & Sons IncBäck, T.T., Fogel, D.B., Michalewicz, Z., Evolutionary computation 1 basic algorithms and operators (2000) Institute of Physiscs Publishing (IOP), Bristol and PhiladelphiaBäck, T.T., Fogel, D.B., Michalewicz, Z., (2000) Evolutionary Computation 2 Advanced Algorithms and Operators, , Institute of Physiscs Publishing IOP, Bristol and PhiladelphiaKennedy, J., Eberhart, R.R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kauffman PublishersDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagAlba, E., Dorronsoro, B., The exploration/exploitation tradeoff in dynamic cellular genetic algorithms (2005) IEEE Transactions on Evolutionary Computation, 9 (2), pp. 126-142. , DOI 10.1109/TEVC.2005.843751Reyes-Sierra, M., Coello Coello, C.A., Multi-objective particle swarm optimizers: A survey of the state-of-the-art (2006) International Journal of Computational Intelligence Research, 2 (3), pp. 287-308Maia, R.D., Pasti, R.R., De Castro, L.N., A bio-inspired strategy to generate diversity in the particle swarm optimization algorithm (2009) First Workshop on Emergent Computing, , Santiago. Jornadas Chilenas de Computación, 2009De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251. , DOI 10.1109/TEVC.2002.1011539, PII S1089778X02060654De Franca, F.O., Von Zuben, F.J., De Castro, L.N., An artificial immune network for multimodal function optimization on dynamic environments (2005) GECCO 2005 - Genetic and Evolutionary Computation Conference, pp. 289-296. , GECCO 2005 - Genetic and Evolutionary Computation Conferenc
An Immunological Density-preserving Approach To The Synthesis Of Rbf Neural Networks For Classification
No full textRadial Basis Function (RBF) neural networks are universal approximators and have been used for a wide range of applications. Aiming at reducing the number of neurons in the hidden layer, for regularization purposes, the center and dispersion of each RBF have to be properly defined by means of competitive learning. Only the output weights will be defined in a supervised manner. One of the drawbacks of such learning methodology, involving unsupervised and supervised learning, is that the centers will be defined so that regions in the input space with a high density of samples tend to be under-represented and those regions with a low density of samples tend to be over-represented. Additionally, few approaches provide a proper and individual indication of the dispersion of each RBF. This paper presents an immune density-preserving algorithm with adaptive radius, called ARIA, to determine the number of centers, their location and the dispersion of each RBF, based only on the available training data set. Considering classification problems, the algorithm to determine the hidden layer is compared to another immune-inspired algorithm called aiNet, K-means and the random choice of centers. The classification accuracy of the final network is compared to another density based approach and a decision tree classifier, C 5.0. The results are reported and analyzed. © 2006 IEEE.929935Hartman, E., Keeler, J., Kowalski, J., Layered neural networks with gaussian hidden units as universal approximations (1990) Neural Computation, 2, pp. 210-215Poggio, T., Girosi, F., (1989) A theory of networks for approximation and learning, , citeseer.ist.psu.edu/article/poggio89theory.html, MIT AI Lab, Tech. Rep. AIM-1140, Online, AvailableMoody, J.E., Darken, C.J., Fast learning in networks of locally tuned processing units (1989) Neural Computation, 1, pp. 281-294Hwang, Y.-S., Bang, S.-Y., An efficient method to construct a radial basis function neural network classifier (1997) Neural Network, 10 (9), pp. 1495-1503Girosi, F., Poggio, M., Poggio, T., Regularization theory and neural networks architectures (1995) Neural Computation, 7 (2), pp. 219-269Orr, M.J.L., Regularization in the selection of radial basis function centers (1995) Neural Computation, 7 (3), pp. 606-623de Castro, L.N., Zuben, F.J.V., Automatic determination of radial basis functions: An immunity-based approach (2001) Int. J. Neural Syst, 11 (6), pp. 523-535Lee, S.-J., Hou, C.-L., An ART-based construction of RBF networks (2002) IEEE-Neural Networks, 13, pp. 1308-1321. , NovLeonardis, A., Bischof, H., An efficient MDL-based construction of RBF networks (1998) Neural Networks, 11 (5), pp. 963-973Bezerra, G.B., Barra, T.V., de Castro, L.N., Zuben, F.J.V., Adaptive radius immune algorithm for data clustering (2005) International Conference on Anificial Immune Systems (ICARIS), pp. 290-303de Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagHaykin, S., (1999) Neural Networks: A Comprehensive Foundation, , Prentice Hall PTRCherkassky, V.S., Mulier, F., (1998) Learning from Data: Concepts, Theory, and Methods, , New York. NY, USA: John Wiley & Sons, IncChen, S., Cowan, C.F.N., Grant, P.M., Orthogonal least squares learning algorithm for radial basis function networks (1991) IEEE Trans. Neural Networks, 2 (2), pp. 302-309. , MarchBezerra, G.B., Barra, T.V., de Castro, L.N., Zuben, F.J.V., Handling data sparseness in gene network reconstruction (2005) ser. Computational Intelligence in Bioinformatics and Computational Biology, pp. 1-8. , Symposium on Computational Intelligence in Bioinformatics and Computational Biology, San DiegoAda, G.L., Nossal, S.G., The clonal-selection theory (1987) Scientific American, 257 (2), pp. 50-57Jerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol. (Inst. Pasteur) 125C, pp. 373-389Seber, G.A.F., (1984) Multivariate Observations, , WileyFoster, M.R., The New Science of Simplicity (2001) ser. Simplicity. Inference and Modelling, 1, pp. 83-117. , Cambridge University Press, chapter 5, ppWang, H., Bell, D.A., Düntsch, I., A density based approach to classification (2003) SAC, pp. 470-474Quinlan, (2003) C 5.0 data mining tool, , http://www.rulequest.com, Online, Availabl
Mlp-based Equalization And Pre-distortion Using An Artificial Immune Network
No full textDue to its universal approximation capability, the multilayer perceptron (MLP) neural network has been applied to several function approximation and classification tasks. Despite its success in solving these problems, its training, when performed by a gradient-based method, is sometimes hindered by the existence of unsatisfactory solutions (local minima). In order to overcome this difficulty, this paper proposes a novel approach to the training of a MLP based on a simple artificial immune network model. The application domain for assessing the performance of the proposed technique is that of digital communications, in particular, the problems of channel equalization and pre-distortion. The obtained simulation results demonstrate that the proposal is capable of efficiently solving the problems tackled. © 2005 IEEE.177182Doering, A., Galicki, M., Witte, H., Structure optimization of neural networks with the a*-algorithm (1997) IEEE Transactions on Neural Networks, 8 (6), pp. 1434-1445Yao, X., Evolutionary artificial neural networks (1995) Encyclopedia of Computer Science and Technology, 33, pp. 137-170. , A. Kent and J. G. Williams, editors, Marcel Dekker Inc., New YorkGudise, V.G., Venayagamoorthy, G.K., Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks (2003) Proceedings of the IEEE Swarm Intelligence Symposium 2003 (SIS 2003), pp. 110-117. , Indianapolis, Indiana, USADe Castro, L.N., Timmis, J.I., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-Verlag, LondonChen, S., Mulgrew, B., Grant, P.M., A clustering technique for digital communications channel equalization using radial basis function networks (1993) IEEE Trans, on Neural Networks, 4 (4), pp. 570-579Haykin, S., (1998) Neural Networks: A Comprehensive Foundation, , Prentice HallIbnkahla, M., Neural network predistortion technique for digital satellite communications (2000) Proceedings of ICASSP, 6, pp. 5-9. , JuneDe Castro, L.N., Timmis, J.I., Artificial immune systems as a novel soft computing paradigm (2003) Soft Computing Journal, 7 (8), pp. 526-544De Attux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal of Applied Signal Processing, 2003 (8), pp. 740:747De Attux, R.R.F., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., A paradigm for blind IIR equalization using the constant modulus criterion and an artificial immune network (2003) Proceedings of the IEEE NNSP, , Toulouse, FranceDe Castro, L.N., Von Zuben, F.J., A hybrid paradigm for weight initialization in supervised feedforward neural network learning (1998) ICS-Workshop on Artificial Intelligence, pp. 30-37. , Tainan, Taiwan, DecemberBattiti, R., First- and second-order methods for learning: Between steepest descent and Newton's method (1992) Neural Computation, 4 (2), pp. 141-16
Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm
Full text linkAmong the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bio informatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional contribution, this work also provides a brief review of ant-based clustering algorithms.292143154Abraham, A., Ramos, V., Web usage mining using artificial ant colony clustering and genetic programming (2003) Proc. of the Congress on Evolutionary Computation (CEC 2003), pp. 1384-1391. , Canberra, IEEE PressBezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithm, , Plenum PressBonabeau, E., Dorigo, M., Théraulaz, G., (1999) Swarm Intelligence from Natural to Artificial Systems, , Oxford University PressCamazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-organization in Biological Systems, , Princeton University PressDe Castro, L.N., Von Zuben, F.J., (2004) Recent Developments in Biologically Inspired Computing, , Idea Group IncDeneubourg, J.L., Goss, S., Sendova-Franks, N.A., Detrain, C., Chrétien, L., The dynamics of collective sorting: Robot-like ant and ant-like robot (1991) Simulation of Adaptive Behavior: from Animals to Animats, pp. 356-365. , J. A. Meyer and S. W. Wilson (eds.). MIT Press/Bradford BooksEveritt, B.S., Landau, S., Leese, M., (2001) Cluster Analysis, , Arnold Publishers, LondonGutowitz, H., Complexity-seeking ants (1993) Proceedings of the Third European Conference on Artificial LifeHandl, J., Knowles, J., Dorigo, M., On the performance of ant-based clustering (2003) Proc. of the 3rd International Conference on Hybrid Intelligent Systems, Design and Application of Hybrid Intelligent Systems, pp. 204-213. , IOS PressHandl, J., Meyer, B., Improved ant-based clustering and sorting in a document retrieval interface (2002) Lecture Notes in Computer Science, 2439, pp. 913-923. , J.J. Merelo, J.L.F. Villacañas, H.G. Beyer, P. Adamis Eds.: Proceedings of the PPSN VII - 7th Int. Conf. on Parallel Problem Solving from Nature, Granada, Spain, Springer-Verlag, BerlinKanade, P., Hall, L.O., Fuzzy ants as a clustering concept (2003) Proc. of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 227-232Kaufman, L., Rousseeuw, P.J., (1990) Finding Groups in Data - An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, , John Wiley & Sons IncKeim, D.A., (2002) Information Visualization and Visual Data Mining: IEEE Transactions on Visuali Zation and Computer Graphics, 7 (1), pp. 100-107Kennedy, J., Eberhart, R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersLabroche, N., Monmarché, N., Venturini, G., A new clustering algorithm based on the chemical recognition system of ants (2002) Proc. of the 15th European Conference on Artificial Intelligence, pp. 345-349. , France, IOS PressLumer, E.D., Faieta, B., Diversity and adaptation in populations of clustering ants (1994) Proceedings of the Third International Conference on the Simulation of Adaptive Behavior: from Animals to Animats, 3, pp. 499-508. , MIT PressMonmarché, N., Slimane, M., Venturini, G., On improving clustering in numerical databases with artificial ants. Advances in artificial life (1999) Lecture Notes in Computer Science, 1674, pp. 626-635. , D. Floreano, J.D. Nicoud, and F. Mondala Eds., Springer-Verlag, BerlinPaton, R., (1994) Computing with Biological Metaphors, , Chapman & HallRamos, V., Merelo, J.J., Self-organized stigmergic document maps: Environment as a mechanism for context learning (2002) AEB'2002, First Spanish Conference on Evolutionary and BioInspired Algorithms, pp. 284-293. , E. Alba, F. Herrera, J.J. Merelo et al. Eds., SpainRamos, V., Muge, F., Pina, P., Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies (2002) Soft-Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications, 87, pp. 500-509. , J. Ruiz-del-Solar, A. Abrahan and M. Köppen Eds. IOS Press, AmsterdamRitter, H., Kohonen, T., Self-organizing semantic maps (1989) Biol. Cybern., 61, pp. 241-254Sherafat, V., De Castro, L.N., Hruschka, E.R., TermitAnt: An ant clustering algorithm improved by ideas from termite colonies (2004) Lecture Notes in Computer Science, 3316, pp. 1088-1093. , Proc. of ICONIP 2004, Special Session on Ant Colony and Multi-Agent SystemsSherafat, V., De Castro, L.N., Hruschka, E.R., The influence of pheromone and adaptive vision on the standard ant clustering algorithm (2004) Recent Developments in Biologically Inspired Computing, pp. 207-234. , L. N. de Castro and F. J. Von Zuben, Chapter IX. Idea Group IncVizine, A.L., De Castro, L.N., Gudwin, R.R., Text document classification using swarm intelligence (2005) Proc. of KIMAS 2005, , CD ROMYeung, K.Y., Medvedovic, M., Bumgarner, R.E., Clustering gene-expression data with repeated measurements (2003) Genome Biology, 4 (5), pp. R34. , articl
Handling Time-varying Tsp Instances
No full textMultimodal optimization algorithms are being adapted to deal with dynamic optimization, mainly due to their ability to provide a faster reaction to unexpected changes in the optimization surface. The faster reaction may be associated with the existence of two important attributes in population-based algorithms devoted to multimodal optimization: simultaneous maintenance of multiple local optima in the population; and self-regulation of the population size along the search. The optimization surface may be subject to variations motivated by one of two main reasons: modification of the objectives to be fulfilled and change in parameters of the problem. An immuneinspired algorithm specially designed to deal with combinatorial optimization is applied here to solve time-varying TSP instances, with the cost of going from one city to the other being a function of time. The proposal presents favorable results when compared to the results produced by a high-performance ant colony optimization algorithm of the literature. © 2006 IEEE.28302837George, A.J.T., Gray, D., Receptor Editing During Affinity Maturation Imm. Today, 20 (4), p. 196Zhou, A., Kang, L., Yan, Z., Solving Dynamic TSP with Evolutionary Approach in Real Time (2003) Proceedings of IEEE Congress on Evolutionary Computation, 2, pp. 951-957Berek, C., Ziegner, M., The Maturation of the Immune Response (1993) Imm. Today, 14 (8), pp. 400-402Blum, C., Dorigo, M., The Hyper-Cube Framework for Ant Colony Optimization (2004) IEEE Transactions on Systems, Man and Cybernetics Part B, 2 (34), pp. 1161-1172Blum, C., Roli, A., Dorigo, M., HC-ACO: The Hyper-Cube Frame-work for Ant Colony Optimization (2001) Proceedings of Meta-Heuristics International Conference, 2, pp. 399-403Eyckelhof, C.J., Snoek, M., Ant Systems for a Dynamic TSP: Ants Caught in a Traffic Jam (2002) Lecture Notes in Computer Science, 2463, pp. 88-99. , Proceedings of ANTS 2002, M. Dorigo, G. Di Caro, M. Samples EdsApplegate, D., Bixby, R., Chvátal, V., Cook, W., History - Solving Travelling Salesman Problem, , http://www.math.princeton.edu/tsp/histmain.html, Available on lineWhitley, D., Rana, S., Heckendorn, R.B., Island Model Genetic Algorithms and Linearly Separable Problems (1997) Lecture Notes in Computer Science, 1305, pp. 109-125. , Proceedings of the AISB Workshop on Evolutionary Computation, D. Corne and J. L. Shapiro Edsde França, F.O., Bio-Inspired Algorithms applied to Dynamic Optimization (2005) Campinas: FEEC/Unicamp, , December, Master Dissertation, School of Electrical and Computer Engineering, State University of Campinas, 139 p, In Portuguesede França, F.O., de Castro, L.N., Von Zuben, F.J., An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments (2005) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 289-296de França, F.O., de Castro, L.N., Von Zuben, F.J., A Max Min Ant System Applied To The Capacitated Clustering Problem (2004) Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 1, pp. 755-764de França, F.O., de Castro, L.N., Von Zuben, F.J., Max Min Ant System and Capacitated p-MediansExtensions and Improved Solutions (2005) Informatica, 29 (2), pp. l63-171Glover, F.W., Kochenberger, G.A., (2002) Handbook of Metaheuristics, , Kluwer Academic PublishersHeller, I., The Travelling Salesmans Problem: Part 1 - Basic Facts (1954) Research Report, , George Washington University Logistics Research Projectde Sousa, J.S., Gomes, L.C.T., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J., An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data (2004) Genetic Programming and Evolvable Machines, 5 (2), pp. 157-179L. N. de Castro and F. J. Von Zuben. aiNet: An Artificial Immune Network for Data Analysis, In Data Mining: A Heuristic Approach, H. A. Abbass, R. A. Sarker, and C. S. Newton (Eds.), Idea Group Publishing, USA, Chapter XII, 2001, pp. 231-259de Castro, L.N., Von Zuben, F.J., Learning and Optimization Using the Clonal Selection Principle (2002) IEEE Transactions on Evolutionary Computation, 3 (6), pp. 239-251de Castro, L.N., Timmis, J., An Artificial Immune Network for Multimodal Function Optimization (2002) Proceedings of the IEEE Congress on Evolutionary Computation, 1, pp. 699-674Dorigo, M., Optimization, Learning and Natural Algorithms (1992), Ph.D.Thesis, Politecnico di Milano, ItalyFarina, M., Deb, K., Amato, P., Dynamic Multiobjective Optimization Problems: Test Cases, Approximation, and Applications (2004) IEEE Transactions on Evolutionary Computation, 8 (5), pp. 425-442. , OctoberGuntsch, M., Middendorf, M., Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP (2001) EvoWorkshops, pp. 213-222Guntsch, M., Middendorf, M., Schmeck, H., An Ant Colony Optimization Approach to Dynamic TSP (2001) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 860-867Flood, M.M., The Traveling-Salesman Problem (1956) Operations Research, 4, pp. 61-75Jerne, N.K., Towards a Network Theory of the Immune System (1974) Ann. Immunol. (Inst. Pasteur), 125 C, pp. 373-389Nakaya, N., Yoshida, H., Miura, M., Genetic Approach to Dynamic Traveling Salesman Problem (2000) Proceedings of International Symposium on Information Theory and Its Applications, pp. 708-711Antia, R., Pilyugin, S.S., Ahmed, R., Models of Immune Memory: On the Role of Cross-Reactive Stimulation, Competition, and Homeostasis in Maintaining Immune Memory (1998) Proc. Nat. Ac. Sc. USA, 95 (25), pp. 14926-14931Lin, S., Kernighan, B.W., An Effective Heuristic Algorithm for the Travelling-Salesman Problem (1973) Operations Research, 21, pp. 498-516Stützle, T., Hoos, H.H., The MAX-MIN Ant System and Local Search for the Traveling Salesman Problem (1997) Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 309-314TSPLIB, A., (1995) Traveling Salesman Problem Library, , http://www.iwr. uni-heidelberg.de/groups/comopt/soft/TSPLIB95/TSPLlB.htmlJin, Y., Branke, J., Evolutionary Optimization in Uncertain Environments - A Survey (2005) IEEE Transactions on Evolutionary Computation, 9 (3), pp. 303-317Liu, Z., Kang, L., A Hybrid Algorithm of n-OPT and GA to Solve Dynamic TSP (2004) Lecture Notes in Computer Science, 3033, pp. 1030-1033. , Proceedings of the Grid and Cooperative Computing, M. Li, X.-H. Sun, Q. Deng, J. Ni Ed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Proposal For Blind Fir Equalization Of Time-varying Channels
No full textThe multimodal and time-varying aspects of blind equalization problems in communication systems are treated here by means of an immune-inspired strategy capable of estimating the coefficients of the FIR equalization filter in an unsupervised manner. The associated optimization problem is solved by means of a population-based search technique characterized by a dynamic control of the population size and diversity maintenance. Static and time-varying channels have been proposed in simulated scenarios, aiming at indicating the tracking capability derived from the adaptive adjustment of the coefficients of the blind equalizer. © 2005 IEEE.914Arenas-Garcia, J., Gómes-Verdejo, V., Martínez-Ramon, M., Filgueiras-Vidal, A.R., Separate-variable adaptive combination of LMS adaptive filters for plant identification (2003) Proceedings of the IEEE XIII Workshop on Neural Network for Signal Processing, pp. 239-248. , ToulouseAttux, R.R.F., Loiola, M.B., Suyama, R., De Castro, L.N., Von Zuben, F.J., Romano, J.M.T., Blind search for optimal wiener equalizers using an artificial immune network model (2003) EURASIP Journal on Applied Signal Processing, 8, pp. 740-747Benveniste, A., Goursat, M., Blind equalizers (1984) IEEE Trans. on Communications, COM-32 (8), pp. 871-883De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-VerlagDe Castro, L.N., Timmis, J., An artificial immune network for multimodal function optimization (2002) Proceedings of the IEEE Congress on Evolutionary Computation, 1, pp. 699-1674. , HawaiiDe França, F.O., Von Zuben, F.J., De Castro, L.N., An artificial immune network for multimodal function optimization on dynamic environments (2005) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 1, pp. 289-296. , Washington D.C., JuneGaspar, A., Collard, P., From GAs to artificial immune systems: Improving adaptation in time dependent optimization (1999) Proceedings of the Congress on Evolutionary Computation (CEC), pp. 1859-1866. , Washington D.CHaykin, S., (1996) Adaptive Filter Theory. 3rd Ed., , Prentice HallHaykin, S., The blind deconvolution problem (1994) Blind Deconvolution, , Haykin, S. (ed.), Prentice-HallJohnson Jr., C.R., Anderson, B.O., Godard blind equalisation surface characteristics: White, zero-mean binary source case (1995) Int. Journal of Adaptive Control and Signal Processing, 9, pp. 301-324Suyama, R., Attux, R.R.F., Romano, J.M.T., Bellanger, M., Relations entre les critère du module constant et de Wiener (2003) Proc. of the GRETSI Symp. on Signal and Image Processing, , Sep., ParisWalker, J., Garrett, S., (2003) Dynamic Function Optimisation: Comparing the Performance of Clonal Selection and Evolutionary Strategies, pp. 273-284. , Lecture Notes in Computer Science 2787. Edinburg
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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