174 research outputs found
Evolving intelligent systems : methodology and applications.
No full textThe newly established concept of evolving intelligent systems is a result of the synergy between conventional systems, neural networks and fuzzy systems as structures for information representation and the real time methods for machine learning. It targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. Neural Networks present a convenient framework for synthesis and analysis of complex non-linear systems. Neuro-fuzzy systems combine the advantages of both areas and often use established machine learning methods for design. One of the important research challenges today is to further develop the intelligent systems theory towards the design of truly intelligent systems with a higher level of flexibility and autonomy that can develop their understanding of the environment and ultimately their intelligence. To address the problems of modelling, control, prediction, classification and data processing in such environments a system must be able to fully adapt its structure rather than adjust its parameters based on a pre-trained and fixed structure. That is, the system must be able to evolve, to self-develop, to self-organize. Evolving intelligent systems are inspired by the idea of system model evolution. They focus on the evolution of an open family of system models representing the system in different situations and operating conditions. In this sense they differ from the traditional evolutionary algorithms. They use inheritance and gradual change with the aim of life-long learning and adaptation, self-organization (including system structure evolution) in order to adapt to the (unknown and unpredictable) environment. Embedded soft computing diagnostics and prognostics algorithms, intelligent agents and controllers are the natural implementation area of evolving systems as a realistic and practical tool for design of real time intelligent systems
Evolving Takagi-Sugeno fuzzy systems from data streams (eTS+).
No full textIt is a well known fact that nowadays we are faced with not only large data sets that we need to process quickly, but with huge data streams (Domingos and Hulten, 2001). Special requirements are also placed by the fast growing sector of autonomous systems where systems that can re-train and adapt ‘on-fly’ are required (Patchett and Sastri, 2007). Similar requirements are enforced by the advanced process industries for self-developing and self-maintaining sensors (Qin et al., 1997). Now they even talk about self-learning industries (EC, 2007). All of these requirements cannot be met by using off-line methods and systems that can only adjust their parameters and/or are linear (Astroem and Wittenmark, 1989). These requirements call for a new type of systems that assumes the structure of non-linear, non-stationary systems to be adaptive and flexible. The author of this chapter started research work in this direction around the turn of the century (Angelov and Buswell, 2001; Angelov, 2002) and this research culminated in proposing with Dr. D. Filev the so called evolving Takagi-Sugeno (eTS) fuzzy system (Angelov and Filev, 2003). Since then a number of improvements of the original algorithm has been done, which require a systematic description in one publication. In this chapter an enhanced version of the eTS algorithm will be described which is called eTS+. It has been tested on a data stream from real engine test bench (data provided courtesy of Dr. E. Lughofer, Linz, Austria). The results demonstrate the superiority of the proposed enhanced approach for modeling real data stream in precision, simplicity and interpretability, and computational resources used. (c) IEEE Press and John Wiley and Son
Evolving Fuzzy Systems, Proc. of the 2006 International Symposium on Evolving Fuzzy Systems EFS'06.
No full textA key characteristic of intelligent systems is their ability to deduce new knowledge, to predict and make decisions. The theoretical underpinning of these capabilities mainly resides in approximate reasoning, which itself is based on fuzzy logic and fuzzy sets. Fuzzy systems have demonstrated the ability to formalize in a computationally efficient manner the approximate reasoning typical of humans. The main challenge today is to design the next generation of intelligent systems able to have a higher level of flexibility and autonomy that can develop their understanding of the environment and ultimately their intelligence. It is to be noticed that the environments in which such systems are required to successfully operate are very often challenging – they are non-stationary, (often unpredictably) changing, and partially or completely unknown. To address the problems of modeling, control, prediction, classification and data processing in such environments a system model must be able to fully adapt, not simply to adjust parameters of a pre-trained and fixed structure. That is, the system model must be able to evolve, to self-develop, to self-organize. A new area that is addressing these challenges is now emerging on the cross-roads of reasoning-based fuzzy systems and evolution-inspired principles of adaptation, life-long learning, self-development and self-organization. It aims to develop systems that are more flexible than conventional adaptive systems that usually assume linearity and fixed structures of the underlying models. The emerging area of evolving fuzzy systems targets non-stationary processes by developing novel on-line learning methods and computationally efficient algorithms for real-time applications. Evolving fuzzy systems are evolution-inspired. They focus on the evolution of individual fuzzy systems. They use inheritance and gradual change with the aim of life-long learning and adaptation, as well as self-organization (including system structure evolution) in order to adapt to the (unknown and unpredictable) environment. This complements well with the established area of genetic fuzzy systems (GFS), that uses techniques based on population-based evolutionary and bio-inspired algorithms (genetic algorithms, genetic programming, evolution strategies, particle swarm optimization methods, bio-memetic approaches, ant colony optimization, etc.) to design fuzzy systems. These two topics make the methodological body of the present Symposium, which is the second in the series of events organized by the GFS Task Force from the Fuzzy Systems Technical Committee, IEEE Computational Intelligence Society. The present Symposium is organized by the Department of Communication Systems, Infolab21, Faculty of Science and Technology, Lancaster University, and technically co-sponsored by the Computational Intelligence Society and Systems, Man, and Cybernetics Society, IEEE, by the International Fuzzy Systems Association (IFSA), and by the European Society on Fuzzy Logic and Technology (EUSFLAT). The Symposium was also generously sponsored by EPSRC-UK arranging a number of student and young researchers’ grants that made possible attendance for a number of talented young people, and co-sponsored by Nokia-UK, BAE Systems, Retail Analytics, and J&S Marine offering a range of ‘best paper’ awards. This volume collects 55 full papers by 122 authors from 23 countries from all continents that have been accepted after being evaluated by at least three independent referees from the International Program Committee. All together, 72 papers authored by 153 authors from 26 countries were submitted and all of them were subjected to anonymous peer review process. The rejection rate of 24% (due to quality or irrelevance) is one of the illustrations of the high quality. The overall number of authors who have contributed to the symposium is another illustration of the International status of the event. The presence of several IEEE Fellows and a number of Senior Members and IEEE Technical Committees members is another fact in support of this qualification. Bearing in mind that this is only the second event of this series, the significant industrial participation (General Electric, USA, Ford Motor Co., USA, Nokia-UK, CEPSA, Spain etc.) illustrates the applicability of the methodologies that are discussed at the Symposium. The structure of the Symposium is composed of two main parts and eleven sessions: i) methodology-based sessions (covering evolving Takagi-Sugeno fuzzy models, evolving neuro-fuzzy systems, evolving fuzzy clustering, evolutionary fuzzy systems, genetic fuzzy systems); and ii) applications-oriented sessions (including industrial applications of real-time evolving fuzzy systems, evolving neuro-fuzzy systems, genetic fuzzy systems, developments in fuzzy systems and a session comprising papers that will discuss and explore the frontiers of computational intelligence and will pose some challenging questions. The discussion will take place in addition to scheduled sessions and networking events during the Round Table. (c) IEEE Pres
An approach to online identification of Takagi-Sugeno fuzzy models
No full textAn approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling
Aplicação de lógica fuzzy no controle de trânsito urbano
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.Este trabalho apresenta o desenvolvimento de uma aplicação de um sistema de controle de tráfego urbano baseado em um modelo utilizando lógica fuzzy. As mudanças ocorridas em nossa sociedade nos últimos anos com o crescimento populacional e o aumento do número de veículos em vias públicas trouxeram problemas como o tráfego intenso e congestionamentos de trânsito. Diariamente perde-se muito tempo em cruzamentos e paradas, e há freqüentes acidentes em vias, trazendo não só prejuízos econômicos, mas também transtornos para o bem estar e para a qualidade de vida das pessoas. Nesta dissertação propõe-se uma contribuição visando amenizar esses inconvenientes. Para tal, a mesma vale-se da ciência da computação e de suas técnicas de inteligência artificial, introduzindo um sistema inteligente de controle de tráfego que melhore o fluxo de atendimento de veículos que concorrem em uma dada interseção, controlando o tempo de ciclo dos semáforos e baseando-se em sistemas atuais existentes de engenharia de tráfego
Flexible Models with Evolving Structure
No full textA type of flexible model in the form of a neural network (NN) with evolving structure is discussed in this study. We refer to models with amorphous structure as flexible models. There is a close link between different types of flexible models: fuzzy models, fuzzy NN, and general regression models. All of them are proven universal approximators and some of them [Takagi-Sugeno fuzzy model with singleton outputs and radial-basis function] are interchangeable. The evolving NN (eNN) considered here makes use of the recently introduced on-line approach to identification of Takagi-Sugeno fuzzy models with evolving structure (eTS). Both TS and eNN differ from the other model schemes by their gradually evolving structure as opposed to the fixed structure models, in which only parameters are subject to optimization or adaptation. The learning algorithm is incremental and combines unsupervised on-line recursive clustering and supervised recursive on-line output parameter estimation. eNN has potential in modeling, control (if combined with the indirect learning mechanism), fault detection and diagnostics etc. Its computational efficiency is based on the noniterative and recursive procedure, which combines the Kalman filter with proper initializations and on-line unsupervised clustering. The eNN has been tested with data from a real air-conditioning installation. Applications to real-time adaptive nonlinear control, fault detection and diagnostics, performance analysis, time-series forecasting, knowledge extraction and accumulation, are possible directions of their use in future research. © 2004 Wiley Periodicals, Inc
On-line evolution of Takagi-Sugeno fuzzy models
No full textEvolving Takagi-Sugeno (eTS) fuzzy models and the method for their on-line identification has been recently introduced for both MISO and MIMO case. In this paper, the mechanism for rule-base evolution, one of the central points of the algorithm together with the recursive clustering and modified recursive least squares (RLS) estimation, is studied in detail. Different scenarios are considered for the rule base upgrade and modification. The radius of influence of each fuzzy rule is considered to be a vector instead of a scalar as in the original eTS approach, allowing different areas of the data space to be covered by each input variable. Simulation results using a well-known benchmark (Mackey-Glass chaotic time-series prediction) are presented. Copyright © 2004 IFA
Guest Editorial : Evolving Fuzzy Systems : preface to the special section.
No full textIt is a well-recognized fact that the theory of fuzzy sets and systems, for the last four decades after the seminal paper by Professor Zadeh [1], has demonstrated its remarkable ability to go beyond conventional information representation. It resulted in a wide range of new formulations of practical problems, such as fuzzy control, fuzzy clustering and classification, fuzzy modeling, and fuzzy optimization [2]. Historically, the design of the fuzzy systems has been initially assumed to be centered on expert knowledge [3]. During the 1990s, a new trend emerged [4], [5] that offered techniques to make use of the experimental data. This data-centered approach can be used to enhance and validate the existing expert knowledge or can also be used to substitute its lack (as is the case with autonomous systems, for example). Neurofuzzy and hybrid learning systems were introduced, where fuzzy representation was integrated into a neural learning architecture to bring linguistic meaning of the learned information [5]. (c) IEEE Pres
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