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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Fuzzy Online Risk Assessment for Distributed Intrusion Prediction and Prevention Systems

Kjetil Haslum; Ajith Abraham; Svein Knapskog Computer Modeling and Simulation, 2008. UKSIM 2008. Tenth International Conference on, 2008

A Distributed Intrusion Prediction and Prevention Systems (DIPPS) not only detects and prevents possible intrusions but also possesses the capability to predict possible intrusions in a distributed network. Based on the DIPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. The key idea of DIPPS is to protect ...


A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances

Camilo P. Tenorio; Francisco de A. T. de Carvalho; Julio T. Pimentel 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces a fuzzy clustering algorithm to partitioning symbolic interval data. The proposed method furnish a fuzzy partition and a prototype (a vector of intervals) for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and ...


Developing a Practical Car Search System Using Fuzzy Theory

Takashi Samatsu; Kie Tachikawa; Yan Shi Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on, 2008

This paper described developing a practical car search system using fuzzy theory. This system aims at supporting car purchasing for a person which is no good with machines as if they ask casually someone who knows more about cars. Unspecific conditions are expressed by the fuzzy set, and the level matching the conditions are expressed by the grade values. To ...


Information Systems Risk Evaluation Based on the AHP-Fuzzy Algorithm

Xuning Peng; Feng Dai Networking and Digital Society, 2009. ICNDS '09. International Conference on, 2009

The AHP-Fuzzy assessment method is a combination which unifies the AHP method and the fuzzy mathematics. AHP can synthesize human's subjective qualitative judgment, form the weighting of each policy- making factor, and avoid appearing the logic reasoning fault in the complex decision-making question. The fuzzy mathematics, simulating the characteristic of human in making the judgment, is good at processing the ...


Stability Analysis of a Class of Discrete Switched Fuzzy Systems

Hong Yang; Xinlai Dai; Xiaodong Liu Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on, 2008

This paper introduces a innovated representation model, namely, a discrete- time switched fuzzy system, which differs from existing ones. In this model, a system is a switched system whose subsystems are all discrete-time T-S fuzzy systems. Using switching technique, the single Lyapunov function method and multiple Lyapunov functions method, the state feedback controllers are built to ensure that the relevant ...


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Educational Resources on Fuzzy systems

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eLearning

Fuzzy Online Risk Assessment for Distributed Intrusion Prediction and Prevention Systems

Kjetil Haslum; Ajith Abraham; Svein Knapskog Computer Modeling and Simulation, 2008. UKSIM 2008. Tenth International Conference on, 2008

A Distributed Intrusion Prediction and Prevention Systems (DIPPS) not only detects and prevents possible intrusions but also possesses the capability to predict possible intrusions in a distributed network. Based on the DIPS sensors, instead of merely preventing the attackers or blocking traffic, we propose a fuzzy logic based online risk assessment scheme. The key idea of DIPPS is to protect ...


A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances

Camilo P. Tenorio; Francisco de A. T. de Carvalho; Julio T. Pimentel 7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper introduces a fuzzy clustering algorithm to partitioning symbolic interval data. The proposed method furnish a fuzzy partition and a prototype (a vector of intervals) for each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and ...


Developing a Practical Car Search System Using Fuzzy Theory

Takashi Samatsu; Kie Tachikawa; Yan Shi Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on, 2008

This paper described developing a practical car search system using fuzzy theory. This system aims at supporting car purchasing for a person which is no good with machines as if they ask casually someone who knows more about cars. Unspecific conditions are expressed by the fuzzy set, and the level matching the conditions are expressed by the grade values. To ...


Information Systems Risk Evaluation Based on the AHP-Fuzzy Algorithm

Xuning Peng; Feng Dai Networking and Digital Society, 2009. ICNDS '09. International Conference on, 2009

The AHP-Fuzzy assessment method is a combination which unifies the AHP method and the fuzzy mathematics. AHP can synthesize human's subjective qualitative judgment, form the weighting of each policy- making factor, and avoid appearing the logic reasoning fault in the complex decision-making question. The fuzzy mathematics, simulating the characteristic of human in making the judgment, is good at processing the ...


Stability Analysis of a Class of Discrete Switched Fuzzy Systems

Hong Yang; Xinlai Dai; Xiaodong Liu Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on, 2008

This paper introduces a innovated representation model, namely, a discrete- time switched fuzzy system, which differs from existing ones. In this model, a system is a switched system whose subsystems are all discrete-time T-S fuzzy systems. Using switching technique, the single Lyapunov function method and multiple Lyapunov functions method, the state feedback controllers are built to ensure that the relevant ...


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IEEE-USA E-Books

  • Artificial Intelligence

    Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Evaluation and validation of decision support aids. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson

  • Neural Networks

    Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Evaluation and validation of decision support aids. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson

  • Fuzzy Logic Switching of FACTS Devices

    This chapter contains sections titled: Introduction Fuzzy Logic Switching Control Scheme [10, 16-19] Evaluation of Control Scheme Multimachine Study System [14] Simulation Studies Conclusions This chapter contains sections titled: References

  • Index

    Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Evaluation and validation of decision support aids. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson

  • Effects of Uncertain Load on Power Network Modeling

    This chapter contains sections titled: Introduction Effects of Data Uncertainty on Power Network Modeling Modeling Uncertain Load in Network Analysis Fuzzy Set Theory Application to External-Network Modeling Concluding Remarks This chapter contains sections titled: References

  • Index

    "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."

  • Mamdani and TS Fuzzy Systems as Functional Approximators

    This chapter contains sections titled: Introduction Fuzzy Controller and Fuzzy Model as Functional Approximators Polynomial Approximation of Continuous Functions Sufficient Approximation Conditions for General MISO Mamdani Fuzzy Systems Sufficient Approximation Conditions for General MISO TS Fuzzy Systems Necessary Approximation Conditions for General SISO Mamdani Fuzzy Systems Necessary Approximation Conditions for General MISO Mamdani Fuzzy Systems Necessary Approximation Conditions for Typical TS Fuzzy Systems Comparison of Minimal Approximator Configuration between Mamdani and TS Fuzzy Systems Conclusions on Mamdani and TS Fuzzy Systems as Functional Approximators Summary Notes and References This chapter contains sections titled: Exercises

  • About the Authors

    Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques now in use with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge representation, knowledge acquisition, and reasoning methodologies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Evaluation and validation of decision support aids. An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson

  • Fuzzy Systems Approach to ShortTerm Power Purchases Considering Uncertain Prices

    This chapter contains sections titled: Introduction Description of the Problem Mathematical Formulation Test Results Discussion Conclusions This chapter contains sections titled: Appendix 1: Notation Appendix 2: Almost Positive Fuzzy Expressions References

  • Introduction

    This chapter contains sections titled: Fuzzy Sets Fuzzy Systems, Complexity, and Ambiguity Fuzziness and Probability When is a Fuzzy Formulation Appropriate? Applications of Fuzzy Systems Beyond Fuzzy Systems: Soft Computing and Computational Intelligence Fuzzy Theory in Electric Power Systems Some Areas of Fuzzy Applications in Power Systems Outline of the Book This chapter contains sections titled: References



Standards related to Fuzzy systems

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Jobs related to Fuzzy systems

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