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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Ensemble of One-Class Classifiers for Network Intrusion Detection System

Anazida Zainal; Mohd Aizaini Maarof; Siti Mariyam Shamsuddin; Ajith Abraham 2008 The Fourth International Conference on Information Assurance and Security, 2008

To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; linear genetic programming (LGP), adaptive neural fuzzy inference system (ANFIS) and random forest (RF). The strengths ...


FPGA implementation of a recurrent neural fuzzy network for on-line temperature control

Chia-Feng Juang; Chao-Hsin Hsu; Yuan-Chang Liou 2005 IEEE International Symposium on Circuits and Systems, 2005

FPGA implementation of a TSK-type recurrent neural fuzzy network (TRNFN) for water bath temperature control is proposed in this paper. The TRNFN is constructed from recurrent fuzzy if-then rules and is built through a concurrent structure and parameter learning. To apply TRNFN to temperature control, the direct inverse control configuration is adopted. For the on-line adaptive control objective, the implemented ...


A method for milk powder spray-drying based on composite fuzzy control technology

Yong Zhang; Xianjiang Shi; Qiang Jing 2009 International Conference on Mechatronics and Automation, 2009

The conventional PID control of most processes has good control effect and robustness. Without adaptive capacity and prone to elicit inertia delay, this method can only be applied in a special working condition. This paper puts forward a composite fuzzy PID control method based on the establishment of fuzzy rules and the compound of PID controller, as well as the ...


Genetically tuned fuzzy scheduling for flexible manufacturing systems

A. M. Erkmen; M. Erbudak; O. Anlagan; O. Unver Proceedings of International Conference on Robotics and Automation, 1997

This paper focuses on the development and implementation of a genetically tuned fuzzy scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on ...


Type-2 fuzzy logic made simple

J. M. Mendel Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on, 2003

First Page of the Article ![](/xploreAssets/images/absImages/01206656.png)


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Educational Resources on Fuzzy systems

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eLearning

Ensemble of One-Class Classifiers for Network Intrusion Detection System

Anazida Zainal; Mohd Aizaini Maarof; Siti Mariyam Shamsuddin; Ajith Abraham 2008 The Fourth International Conference on Information Assurance and Security, 2008

To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; linear genetic programming (LGP), adaptive neural fuzzy inference system (ANFIS) and random forest (RF). The strengths ...


FPGA implementation of a recurrent neural fuzzy network for on-line temperature control

Chia-Feng Juang; Chao-Hsin Hsu; Yuan-Chang Liou 2005 IEEE International Symposium on Circuits and Systems, 2005

FPGA implementation of a TSK-type recurrent neural fuzzy network (TRNFN) for water bath temperature control is proposed in this paper. The TRNFN is constructed from recurrent fuzzy if-then rules and is built through a concurrent structure and parameter learning. To apply TRNFN to temperature control, the direct inverse control configuration is adopted. For the on-line adaptive control objective, the implemented ...


A method for milk powder spray-drying based on composite fuzzy control technology

Yong Zhang; Xianjiang Shi; Qiang Jing 2009 International Conference on Mechatronics and Automation, 2009

The conventional PID control of most processes has good control effect and robustness. Without adaptive capacity and prone to elicit inertia delay, this method can only be applied in a special working condition. This paper puts forward a composite fuzzy PID control method based on the establishment of fuzzy rules and the compound of PID controller, as well as the ...


Genetically tuned fuzzy scheduling for flexible manufacturing systems

A. M. Erkmen; M. Erbudak; O. Anlagan; O. Unver Proceedings of International Conference on Robotics and Automation, 1997

This paper focuses on the development and implementation of a genetically tuned fuzzy scheduler (GTFS) for heterogeneous FMS under uncertainty. The scheduling system takes input from a table and creates an optimum master schedule. The GTFS uses fuzzy rulebase and inferencing where fuzzy sets are generated by a genetic algorithm to tune the optimization. The fuzzy optimization is based on ...


Type-2 fuzzy logic made simple

J. M. Mendel Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference on, 2003

First Page of the Article ![](/xploreAssets/images/absImages/01206656.png)


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IEEE-USA E-Books

  • Introduction

    This introduction presents an overview of key concepts discussed in the subsequent chapters of this book. The book reviews the applications of power electronics. It is organized into three parts, each dealing with one of the three main topics, high voltage direct current (HVDC), flexible alternating current transmission systems (FACTS), and Artificial intelligence (AI). The book concerns with the theory of HVDC transmission with a comprehensive description of the semiconductor devices and power electronic converters. It presents a class of power electronic applications called the FACTS devices. The book is devoted to applications of AI and computational intelligence (CI) techniques to power systems with a comprehensive overview of the AI and CI techniques that help realize the vision of a smart grid. These techniques include artificial neural networks, fuzzy systems, multiagent systems, heuristic optimization, and unsupervised learning.

  • Evolving Fuzzy Modeling Using Participatory Learning

    This chapter contains sections titled: Introduction Evolving Fuzzy Systems Evolving Fuzzy Participatory Learning Experiments with a Benchmark Problem Electric Load Forecasting Conclusion Acknowledgments References

  • Fuzzy Information Approaches to Equipment Condition Monitoring and Diagnosis

    This chapter contains sections titled: Introduction Expert Systems and Equipment Diagnostics The Fuzzy Information Approach An Extended Example A Proposed Implementation Evaluation, Learning, and Information Measures Performance Summary and Discussion This chapter contains sections titled: Acknowledgments References

  • Alternative Approaches

    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

  • Index

    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.

  • Appendix C: Biologically Inspired Optimization

    No abstract.

  • 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

  • Fuzzy Set Theory

    Relevant concepts of fuzzy set theory are introduced in this chapter to make the book self-contained. The focus is on standard fuzzy sets, but an overview of well-known nonstandard fuzzy sets is also included. The following concepts are covered for standard fuzzy sets: -cut representation, operations on fuzzy sets, fuzzy numbers and fuzzy arithmetic, fuzzy relations, approximate reasoning, and basic ideas regarding fuzzy systems.

  • Introduction

    This chapter contains sections titled: Fuzzy Systems Expert Knowledge When and When Not to Use Fuzzy Control Control Interconnection of Several Subsystems Identification and Adaptive Control Summary Exercises

  • 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.



Standards related to Fuzzy systems

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