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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An effective algorithm for discovering fuzzy rules in relational databases

Wai-Ho Au; K. C. C. Chan 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 1998

We present a novel technique, called F-APACS, for discovering fuzzy association rules in relational databases. F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The definitions of these linguistic terms are based on fuzzy set theory and the association rules expressed in them are called fuzzy association rules. To discover these rules, F-APACS utilizes an objective interestingness measure ...


Robust fuzzy output feedback control for chaotic systems with time delay

Da-un Jeong; Dongyeop Kang; Sangsu Yeh; Sangchul Won 2009 ICCAS-SICE, 2009

In this paper, a delay dependent robust fuzzy output feedback control is considered for time-delay chaotic systems with disturbances. Observer based controller is constructed by the T-S fuzzy model. The control algorithm is proposed to guarantee the Hinfin performance of the time-delay chaotic systems. Linear matrix inequality (LMI) method is used to obtain controller and observer gains. To find the ...


A Novel Graph-Based Image Annotation Refinement Algorithm

Liu Zheng 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009

A novel graph-based approach to automatically refine image annotation is presented in this paper. Given an unannotated image, a set of candidate annotations is extracted by the existing image annotation method. Then, each candidate annotation is converted to vertex of a graph and the semantic similarity between two candidate annotations is used as edge weight. Next, a heuristics graph algorithm ...


Robust adaptive reliable tracking control for a class of uncertain switched fuzzy systems

Le Zhang; Shuliang Li; Hong Yang 2009 Chinese Control and Decision Conference, 2009

The problem, which combines the robust adaptive reliable control of uncertain stitched fuzzy system, is studied. Address the robust adaptive reliable control issue of nonlinear systems based on the uncertain switched fuzzy model, which each subsystem is an uncertain Takagi-Sugeno (T-S) fuzzy model. Introducing parametric uncertainly terms into building the T-S model for nonlinear systems, the uncertain switched fuzzy model ...


A Fuzzy Approach to a Hydroelectric System Operation with Interchange Constraints

L. M. V. G. Pinto; C. G. Fonseca; A. B. Valle Proceedings. Joint International Power Conference Athens Power Tech,, 1993

First Page of the Article ![](/xploreAssets/images/absImages/00686914.png)


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Educational Resources on Fuzzy systems

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eLearning

An effective algorithm for discovering fuzzy rules in relational databases

Wai-Ho Au; K. C. C. Chan 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228), 1998

We present a novel technique, called F-APACS, for discovering fuzzy association rules in relational databases. F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The definitions of these linguistic terms are based on fuzzy set theory and the association rules expressed in them are called fuzzy association rules. To discover these rules, F-APACS utilizes an objective interestingness measure ...


Robust fuzzy output feedback control for chaotic systems with time delay

Da-un Jeong; Dongyeop Kang; Sangsu Yeh; Sangchul Won 2009 ICCAS-SICE, 2009

In this paper, a delay dependent robust fuzzy output feedback control is considered for time-delay chaotic systems with disturbances. Observer based controller is constructed by the T-S fuzzy model. The control algorithm is proposed to guarantee the Hinfin performance of the time-delay chaotic systems. Linear matrix inequality (LMI) method is used to obtain controller and observer gains. To find the ...


A Novel Graph-Based Image Annotation Refinement Algorithm

Liu Zheng 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009

A novel graph-based approach to automatically refine image annotation is presented in this paper. Given an unannotated image, a set of candidate annotations is extracted by the existing image annotation method. Then, each candidate annotation is converted to vertex of a graph and the semantic similarity between two candidate annotations is used as edge weight. Next, a heuristics graph algorithm ...


Robust adaptive reliable tracking control for a class of uncertain switched fuzzy systems

Le Zhang; Shuliang Li; Hong Yang 2009 Chinese Control and Decision Conference, 2009

The problem, which combines the robust adaptive reliable control of uncertain stitched fuzzy system, is studied. Address the robust adaptive reliable control issue of nonlinear systems based on the uncertain switched fuzzy model, which each subsystem is an uncertain Takagi-Sugeno (T-S) fuzzy model. Introducing parametric uncertainly terms into building the T-S model for nonlinear systems, the uncertain switched fuzzy model ...


A Fuzzy Approach to a Hydroelectric System Operation with Interchange Constraints

L. M. V. G. Pinto; C. G. Fonseca; A. B. Valle Proceedings. Joint International Power Conference Athens Power Tech,, 1993

First Page of the Article ![](/xploreAssets/images/absImages/00686914.png)


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IEEE-USA E-Books

  • Fuzzy Systems

    Fuzzy logic was transformed from a 'buzz word' to an important technological area, with many publications in conferences and transactions. Many Japanese products applying fuzzy concepts, such as household appliances and electronic equipment, have been manufactured. This chapter presents a relation of classical and fuzzy sets and concepts of fuzzy graphs and fuzzy relations. It also presents the foundation of fuzzy logic, with the fuzzification inference process and defuzzification process. The chapter explores a popular fuzzy model to represent complex systems and an application of fuzzy control. A fuzzy decision system provides a mapping between input and output variables. The chapter also mentions some application areas of fuzzy techniques. It describes principal definitions and concepts of the classical Cantor's sets that are well¿¿?known in order to guide the readers about the notation and a comparison with the similar concepts in fuzzy sets theory.

  • 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.

  • Appendix A: Mathematical Prerequisites

    No abstract.

  • Fuzzy Modeling: Principles and Methodology

    This chapter contains sections titled: The architectural blueprint of fuzzy models Key phases of the development and use of fuzzy models Main categories of fuzzy models: an overview Verification and validation of fuzzy models Conclusions Exercises and Problems Historical notes References

  • Basic Fuzzy Set Theory

    Fuzzy set theory and fuzzy logic provide a different way to view the problem of modeling uncertainty and offer a wide range of computational tools to aid decision making. The mathematical basis for formal fuzzy logic can be found in infinite-valued logics, first studied by the Polish logician Jan Lukasiewicz in the 1920s. While the big economic impact of fuzzy set theory and fuzzy logic centers on control, particularly in consumer electronics, there has been, and continues to be, much research and application of these technologies in pattern recognition, information fusion, data mining, and automated decision making. All fuzzy set theory is based on the concept of a membership function. In many cases, the membership functions take on specific functional forms such as triangular, trapezoidal, S-functions, pi-functions, sigmoids, and even Gaussians for convenience in representation and computation. A neural network also acts as a membership function.

  • Genetic Algorithms

    Genetic algorithms (GAs) constitute a branch of the science of evolutionary computation (EC), which itself is a branch of Computational Intelligence (CI) together with neurocomputing and fuzzy systems. The EC science is about algorithms and techniques that try to simulate the biological evolution of species in nature as well as evolution¿¿?developed biological mechanisms and collective behaviors. Genetic algorithms are probably the most studied and 'evolved' class of evolution¿¿?inspired methods. GAs use an abstract flow diagram, an extracted form Darwin's theory that utilizes the most important 'key' principles of natural evolution. This is the reason why they manage to inherit the enormous exploration power of their natural counterpart and exhibit such effectiveness in discovering high quality solutions in vast search spaces. The internal mechanism of a GA employs a number of parameters that can determine the specific nature of the algorithm's search strategy, and affect the overall optimization performance of a GA.

  • Appendix B: Neurocomputing

    No abstract.

  • RuleBased Fuzzy Models

    This chapter contains sections titled: Fuzzy rules as a vehicle of knowledge representation General categories of fuzzy rules and their semantics Syntax of fuzzy rules Basic functional modules: rule base, database, and inference scheme Types of rule-based systems and architectures Approximation properties of fuzzy rule-based models Development of rule-based systems Parameter estimation procedure for functional rule-based systems Design issues of rule-based systems - consistency, completeness, and the curse of dimensionality The curse of dimensionality in rule-based systems Development scheme of fuzzy rule-based models Conclusions Exercises and problems Historical notes References

  • Fuzzy Relations and Fuzzy Logic Inference

    This chapter describes the background necessary to understand and construct fuzzy logic inference systems for decision-making problems and control applications. Fuzzy logic begins with the concept of a linguistic variable. Once we have this fundamental concept of a linguistic variable, we can build the machinery necessary for fuzzy logic inference. The chapter provides mechanisms for making deductions that are all based on the concept of fuzzy relations. There are many examples of direct applications of fuzzy relations, and the chapter concentrate on the main use, that of providing an engine for logical inference in a fuzzy rule-based system. IF-THEN rules form the basis of a fuzzy logic inference system. Systems of fuzzy rules can be built or learned to perform control functions, but also to work as a pattern classifier. These systems in many cases behave like statistical classifiers, but can also encode human knowledge directly into the structure in a linguistically pleasing manner.

  • 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



Standards related to Fuzzy systems

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Jobs related to Fuzzy systems

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