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Neural Network Systems Techniques and Applications, Fuzzy Logic and Expert Systems Applications, Volume 6
 
 
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Neural Network Systems Techniques and Applications, Fuzzy Logic and Expert Systems Applications, Volume 6 [Hardcover]

Cornelius T. Leondes (Author, Series Editor)

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Book Description

0124438660 978-0124438668 December 25, 1997 1
This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies.

Key Features
* Fuzzy system techniques applied to neural networks for modeling and control
* Systematic design procedures for realizing fuzzy neural systems
* Techniques for the design of rule-based expert systems
* Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets
* System identification and control
* Nonparametric, nonlinear estimation
Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies

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Editorial Reviews

From the Back Cover

Inspired by the structure of the human brain, artificial neural networks have found many applications due to their ability to solve cumbersome or intractable problems by learning directly from data. Neural networks can adapt to new environments by learning, and deal with information that is noisy, inconsistent, vague, or probabilistic. This volume of Neural Network Systems Techniques and Applications is devoted to the integration of Fuzzy Logic and Expert Systems Applications.

About the Author

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.


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Inside This Book (learn more)
First Sentence:
Fuzzy logic and neural networks have been combined in a variety of ways. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
input fuzzy terms, input fuzzy partition, interval connection weights, conventional fuzzy systems, fuzzy rule basis, output fuzzy regions, fuzzy biases, corresponding control rules, fuzzified neural networks, fuzzy neural network controller, proposed fuzzy neural network, model reference control system, layer whose neurons, proposed fuzzy system, semiphysical model, local linear systems, sparse singularities, local system parameters, regressor candidates, fuzzy regression model, nonlinear nonparametric models, quantization layer, connectionist expert systems, interval regression model, fuzzy classification method
Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, New York, The Netherlands, Sara Burgerhartstraat, Technical Report, Systems Man Cybernet, Academic Press, Aldo Aiello, International Joint Conference, San Diego, Kluwer Academic, Systems Man Cvbernet, Chu Kwong Chak, Japan Soc, Campus Universitaire de Beaulieu, Rennes Cedex France, Stanford University, Systemes Aleatoires, Artificial Intell, Department of Statistics, Englewood Cliffs, False Figure, Machine Intell, Pattern Anal, Actual Modeled Data
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