Conferences related to Fuzzy systems

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2014 IEEE Congress on Evolutionary Computation (CEC)

IEEE Congress on Evolutionary Computation is the largest technical event in the field of evolutionary computation. In 2014, International Joint Conference on Neural Networks will be part of the 2104 IEEE World Congress on Computational Intelligence.

  • 2013 IEEE Congress on Evolutionary Computation (CEC)

    CEC 2013 will bring together researchers and practitioners in the field of evolutionary computation and computational intelligence from around the globe. Theory, applications, algorithmic developments and all other aspects of evolutionary computation and related areas (i.e., any other bio-inspired metaheuristics) are welcome to contribute to this conference.

  • 2012 IEEE Congress on Evolutionary Computation (CEC)

    The annual IEEE CEC is one of the leading events in the field of evolutionary computation.

  • 2011 IEEE Congress on Evolutionary Computation (CEC)

    Annual Congress on Evolutionary Computation.

  • 2010 IEEE Congress on Evolutionary Computation (CEC)

  • 2009 IEEE Congress on Evolutionary Computation (CEC)

    CEC 2009 will feature a world-class conference that aims to bring together researchers and practitioners in the field of evolutionary computation and computational intelligence from all around the globe. Technical exchanges within the research community will encompass keynote speeches, special sessions, tutorials, panel discussions as well as poster presentations.

  • 2008 IEEE Congress on Evolutionary Computation (CEC)

    Composed of the International Joint Conference on Neural Networks (IJCNN), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and IEEE Congress on Evolutionary Computation (CEC), WCCI 2008 will be the largest technical event on computational intelligence in the world


2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

IEEE International Conference on Fuzzy Systems is the largest technical event in the field of fuzzy systems. In 2014, International Joint Conference on Neural Networks will be part of the 2104 IEEE World Congress on Computational Intelligence.


2014 IEEE International Conference on Systems, Man and Cybernetics - SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.


2014 IEEE World Congress on Computational Intelligence (WCCI)

IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. IEEE WCCI 2014 will host three conferences: The 2014 International Joint Conference on Neural Networks, the 2014 IEEE International Conference on Fuzzy Systems, and the 2014 IEEE Congress on Evolutionary Computation.

  • 2012 IEEE World Congress on Computational Intelligence (WCCI)

    Computational Intelligence, Evolutionary Computation, Neural Networks, Fuzzy Systems, Swarm Intelligence, Nature Inspired Computing

  • 2010 IEEE World Congress on Computational Intelligence (WCCI)

    The WCCI is the best-known academic Olympic event in the computational intelligence community. Comprised of three international events, the International Joint Conference on Neural Networks (IJCNN), the IEEE International Conference on C (FUZZ-IEEE), and the IEEE Congress on Evolutionary Computation (CEC), WCCI provides a venue to foster technical exchange, renew friendships, and establish new connections


2013 IEEE 10th International Conference on Networking, Sensing and Control (ICNSC)

The main theme of the conference is Technology for efficient green networks . It will provide a remarkable opportunity for the academic and industrial communities to address new challenges and share solutions, and discuss future research directions.

  • 2012 9th IEEE International Conference on Networking, Sensing and Control (ICNSC)

    This conference will provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions in the area of intelligent transportation systems and networks as well other areas of networking, sensing and control. It will feature plenary speeches, industrial panel sessions, funding agency panel sessions, interactive sessions, and invited/special sessions. Contributions are expected from academia, industry, and government agencies.

  • 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC)

    The main theme of the conference is Next Generation Infrastructures . Infrastructures are the backbone of the economy and society. Especially the network bound infrastructures operated by public utilities and network industries provide essential services that are enabling for almost every economic and social activity. The crucial role of the infrastructure networks for energy and water supply, transportation of people and goods, and provision of telecommunication and information services.

  • 2010 International Conference on Networking, Sensing and Control (ICNSC)

    Provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions.


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Periodicals related to Fuzzy systems

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Systems, Man and Cybernetics, Part A, IEEE Transactions on

Systems engineering, including efforts that involve issue formnaulations, issue analysis and modeling, and decision making and issue interpretation at any of the life-cycle phases associated with the definition, development, and implementation of large systems. It will also include efforts that relate to systems management, systems engineering processes and a variety of systems engineering methods such as optimization, modeling and simulation. ...


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...



Most published Xplore authors for Fuzzy systems

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Xplore Articles related to Fuzzy systems

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Optimal power system steady-state security regions with fuzzy constraints

J. Z. Zhu 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), 2002

This paper proposes a new approach to compute the maximal power system steady- state security regions using optimization approach. The maximal steady-state security region-hyperbox-is directly computed through a linear programming (LP) model, in which the upper and lower limits of each component forming a hyperbox are taken as unknown variables, and the objective is to maximize the sum of the ...


An optimized fuzzy logic-based control of static VAr compensator in a power system with wind generation

M. F. Kandlawala; T. T. Nguyen 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009

An optimal controller for a shunt-connected static VAr compensator (SVC) has been developed for improving the dynamic performance of a power system with wind-turbine generators. The fuzzy-logic control complements the voltage control function, and provides damping in the system. A constrained- optimization design procedure for determining the optimal values of the linguistic variables for forming the crisp output of the ...


Intelligent Control of Grid-Connected Microgrids: An Adaptive Critic-Based Approach

Sima Seidi Khorramabadi; Alireza Bakhshai IEEE Journal of Emerging and Selected Topics in Power Electronics, 2015

This paper presents an adaptive and intelligent power control approach for microgrid systems in the grid-connected operation mode. The proposed critic- based adaptive control system contains a neuro-fuzzy controller and a fuzzy critic agent. The fuzzy critic agent employs a reinforcement learning algorithm based on neuro-dynamic programming. The system feedback is made available to the critic agent's input as the ...


Robust fault estimation and accommodation for discrete-time Takagi-Sugeno fuzzy systems

Weixin Han; Yu Zhang; Zhenhua Wang; Yi Shen Proceedings of the 33rd Chinese Control Conference, 2014

This paper proposes a new integrated observer-based fault estimation and accommodation strategy for discrete-time Takagi-Sugeno (T-S) fuzzy systems subject to actuator faults. Specifically, a robust observer is designed to simultaneously estimate the state and the actuator fault. Using the fault estimation, a robust fault tolerant controller based on the concept of input- to-state stability (ISS) is proposed to achieve fault ...


Profit variety for inventory decision changing in supply chain under fuzzy demand environment

Xiao-Bin Wang 2010 International Conference on Machine Learning and Cybernetics, 2010

In this paper, it is investigated for the profit variety in supply chain when inventory decision changes from downstream to upstream, and in which the demand of market is assumed to be a fuzzy variable. Firstly, the profit variety of retailer and supplier are compared when the inventory decision changes from retailer to supplier, respectively. Then, some propositions are given ...


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Educational Resources on Fuzzy systems

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eLearning

Optimal power system steady-state security regions with fuzzy constraints

J. Z. Zhu 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), 2002

This paper proposes a new approach to compute the maximal power system steady- state security regions using optimization approach. The maximal steady-state security region-hyperbox-is directly computed through a linear programming (LP) model, in which the upper and lower limits of each component forming a hyperbox are taken as unknown variables, and the objective is to maximize the sum of the ...


An optimized fuzzy logic-based control of static VAr compensator in a power system with wind generation

M. F. Kandlawala; T. T. Nguyen 2009 Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009

An optimal controller for a shunt-connected static VAr compensator (SVC) has been developed for improving the dynamic performance of a power system with wind-turbine generators. The fuzzy-logic control complements the voltage control function, and provides damping in the system. A constrained- optimization design procedure for determining the optimal values of the linguistic variables for forming the crisp output of the ...


Intelligent Control of Grid-Connected Microgrids: An Adaptive Critic-Based Approach

Sima Seidi Khorramabadi; Alireza Bakhshai IEEE Journal of Emerging and Selected Topics in Power Electronics, 2015

This paper presents an adaptive and intelligent power control approach for microgrid systems in the grid-connected operation mode. The proposed critic- based adaptive control system contains a neuro-fuzzy controller and a fuzzy critic agent. The fuzzy critic agent employs a reinforcement learning algorithm based on neuro-dynamic programming. The system feedback is made available to the critic agent's input as the ...


Robust fault estimation and accommodation for discrete-time Takagi-Sugeno fuzzy systems

Weixin Han; Yu Zhang; Zhenhua Wang; Yi Shen Proceedings of the 33rd Chinese Control Conference, 2014

This paper proposes a new integrated observer-based fault estimation and accommodation strategy for discrete-time Takagi-Sugeno (T-S) fuzzy systems subject to actuator faults. Specifically, a robust observer is designed to simultaneously estimate the state and the actuator fault. Using the fault estimation, a robust fault tolerant controller based on the concept of input- to-state stability (ISS) is proposed to achieve fault ...


Profit variety for inventory decision changing in supply chain under fuzzy demand environment

Xiao-Bin Wang 2010 International Conference on Machine Learning and Cybernetics, 2010

In this paper, it is investigated for the profit variety in supply chain when inventory decision changes from downstream to upstream, and in which the demand of market is assumed to be a fuzzy variable. Firstly, the profit variety of retailer and supplier are compared when the inventory decision changes from retailer to supplier, respectively. Then, some propositions are given ...


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IEEE-USA E-Books

  • Introduction and Single-Layer Neural Networks

    Neural networks are potentially massively parallel distributed structures and have the ability to learn and generalize. The neuron is the information processing unit of a neural network and the basis for designing numerous neural networks. The most fundamental network architecture is a single-layer neural network, where the single-layer refers to the output layer of computation neurons. This chapter introduces Rosenblatt's neuron. Rosenblatt's perceptron occupies a special place in the historical development of neural networks. The chapter also considers the performance of the perceptron network and is in a position to introduce the perceptron learning rule. This learning rule is an example of supervised training, in which the learning rule is provided with a set of examples of proper network behavior. Finally the chapter further discusses activation function and its types, including a threshold function, or Heaviside function and sigmoid function.

  • Multilayer Neural Networks and Backpropagation

    A computationally effective method for training the multilayer perceptrons is the backpropagation algorithm, which is regarded as a landmark in the development of neural network. This chapter presents two different learning methods, batch learning and online learning, on the basis of how the supervised learning of the multilayer perceptron is actually performed. The essence of backpropagation learning is to encode an input-output mapping into the synaptic weights and thresholds of a multilayer perceptron. It is hoped that the network becomes well trained so that it learns enough about the past to generalize to the future. The chapter concludes with cross-validation and generalization. Cross-validation is appealing particularly when people have to design a large neural network with good generalization as the goal in different ways. Generalization is assumed that the test data are drawn from the same population used to generate the training data.

  • Radial-Basis Function Networks

    This chapter focuses on the radial-basis function (RBF) network as an alternative to multilayer perceptrons. It will be interesting to find that in a multilayer perceptron, the function approximation is defined by a nested set of weighted summations, while in a RBF network, the approximation is defined by a single weighted sum. The chapter focuses on the use of a Gaussian function as the radial-basis function. The reason behind the choice of the Gaussian function as the radial-basis function in building RBF networks is that it has many desirable properties, which will become evident as the discussion progresses. It is important to point out that RBF networks and multilayer perceptrons can be trained in alternative ways besides those presented. For multilayer perceptrons, the backpropagation algorithm is simple to compute locally and it performs stochastic gradient descent in weight space when the algorithm is implemented in an online learning mode.

  • Index

    The emerging, powerful fuzzy control paradigm has led to the worldwide success of countless commercial products and real-world applications. Fuzzy control is exceptionally practical and cost-effective due to its unique ability to accomplish tasks without knowing the mathematical model of the system, even if it is nonlinear, time varying and complex. Nevertheless, compared with the conventional control technology, most fuzzy control applications are developed in an ad hoc manner with little analytical understanding and without rigorous system analysis and design. Fuzzy Control and Modeling is the only book that establishes the analytical foundations for fuzzy control and modeling in relation to the conventional linear and nonlinear theories of control and systems. The coverage is up-to-date, comprehensive, in-depth and rigorous. Numeric examples and applications illustrate the utility of the theoretical development. Important topics discussed include: Structures of fuzzy controllers/models with respect to conventional fuzzy controllers/models Analysis of fuzzy control and modeling in relation to their classical counterparts Stability analysis of fuzzy systems and design of fuzzy control systems Sufficient and necessary conditions on fuzzy systems as universal approximators Real-time fuzzy control systems for treatment of life-critical problems in biomedicine Fuzzy Control and Modeling is a self-contained, invaluable resource for professionals and students in diverse technical fields who aspire to analytically study fuzzy control and modeling.

  • Granular Models and HumanCentric Computing

    This chapter contains sections titled: The cluster-based representation of the input-output mappings Context-based clustering in the development of granular models Granular neuron as a generic processing element in granular networks Architecture of granular models based on conditional fuzzy clustering Refinements of granular models Incremental granular models Human-centric fuzzy clustering Participatory Learning in fuzzy clustering Conclusions Exercises and problems Historical notes References

  • Recurrent Neural Networks

    This chapter considers a class of neural networks that have a recurrent structure, including Grossberg network, Hopfield network, and cellular neural networks. The Hopfield network is a form of recurrent artificial neural network invented by John Hopfield in 1982. It consists of a set of neurons and a corresponding set of unit time delays, formatting a multiple-loop feedback system. There are three components to the Grossberg network: Layer 1, Layer 2, and the adaptive weights. Layer 1 is a rough model of the operation of the retina, while Layer 2 represents the visual cortex. Cellular neural networks contain linear and nonlinear circuit elements, which typically are linear capacitors, linear resistors, linear and nonlinear controlled sources, and independent sources. The chapter also describes the mathematical model of a nonlinear dynamic system, and discusses some of the important issues involved in neurodynamics.

  • Fuzzy Systems

    This chapter contains sections titled: Motivation and Definitions Integration of Fuzzy Systems with Evolutionary Techniques An Illustrative Example of a Hybrid System Conclusions References

  • About the Editor

    This book offers an introduction to applications of fuzzy system theory to selected areas of electric power engineering. It presents theoretical background material from a practical point of view and then explores a number of applications of fuzzy systems. Most recently, there has been a tremendous surge in research and application articles on this subject. Until now though, there have been no books that put together a practical guide to the fundamentals and applications aspects. Electric Power Applications of Fuzzy Systems presents, under one cover, original contributions by authors who have pioneered in the application of fuzzy system theory to the electric power engineering field. Each chapter contains both an introduction to and a state- of-the-art review of each application area.

  • About the Editors

    "IEEE Press is proud to present the first selected reprint volume devoted to the new field of intelligent signal processing (ISP). ISP differs fundamentally from the classical approach to statistical signal processing in that the input-output behavior of a complex system is modeled by using "intelligent" or "model-free" techniques, rather than relying on the shortcomings of a mathematical model. Information is extracted from incoming signal and noise data, making few assumptions about the statistical structure of signals and their environment. Intelligent Signal Processing explores how ISP tools address the problems of practical neural systems, new signal data, and blind fuzzy approximators. The editors have compiled 20 articles written by prominent researchers covering 15 diverse, practical applications of this nascent topic, exposing the reader to the signal processing power of learning and adaptive systems. This essential reference is intended for researchers, professional engineers, and scientists working in statistical signal processing and its applications in various fields such as humanistic intelligence, stochastic resonance, financial markets, optimization, pattern recognition, signal detection, speech processing, and sensor fusion. Intelligent Signal Processing is also invaluable for graduate students and academics with a background in computer science, computer engineering, or electrical engineering. About the Editors Simon Haykin is the founding director of the Communications Research Laboratory at McMaster University, Hamilton, Ontario, Canada, where he serves as university professor. His research interests include nonlinear dynamics, neural networks and adaptive filters and their applications in radar and communications systems. Dr. Haykin is the edito r for a series of books on "Adaptive and Learning Systems for Signal Processing, Communications and Control" (Publisher) and is both an IEEE Fellow and Fellow of the Royal Society of Canada. Bart Kosko is a past director of the University of Southern California's (USC) Signal and Image Processing Institute. He has authored several books, including Neural Networks and Fuzzy Systems, Neural Networks for Signal Processing (Publisher, copyright date) and Fuzzy Thinking (Publisher, copyright date), as well as the novel Nanotime (Publisher, copyright date). Dr. Kosko is an elected governor of the International Neural Network Society and has chaired many neural and fuzzy system conferences. Currently, he is associate professor of electrical engineering at USC."

  • About the Author

    The emerging, powerful fuzzy control paradigm has led to the worldwide success of countless commercial products and real-world applications. Fuzzy control is exceptionally practical and cost-effective due to its unique ability to accomplish tasks without knowing the mathematical model of the system, even if it is nonlinear, time varying and complex. Nevertheless, compared with the conventional control technology, most fuzzy control applications are developed in an ad hoc manner with little analytical understanding and without rigorous system analysis and design. Fuzzy Control and Modeling is the only book that establishes the analytical foundations for fuzzy control and modeling in relation to the conventional linear and nonlinear theories of control and systems. The coverage is up-to-date, comprehensive, in-depth and rigorous. Numeric examples and applications illustrate the utility of the theoretical development. Important topics discussed include: Structures of fuzzy controllers/models with respect to conventional fuzzy controllers/models Analysis of fuzzy control and modeling in relation to their classical counterparts Stability analysis of fuzzy systems and design of fuzzy control systems Sufficient and necessary conditions on fuzzy systems as universal approximators Real-time fuzzy control systems for treatment of life-critical problems in biomedicine Fuzzy Control and Modeling is a self-contained, invaluable resource for professionals and students in diverse technical fields who aspire to analytically study fuzzy control and modeling.



Standards related to Fuzzy systems

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Jobs related to Fuzzy systems

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