Applications of fuzzy theory (often referred to as "fuzzy logic") are maturing and multiplying at a phenomenal rate, and a comprehensive treatment of these real-world techniques and applications is now very timely. Unlike traditional computer logic involving clear true or false decisions, a fuzzy logic system chooses what is most true after "considering" several contributing and possibly conflicting variables. Examples of practical devices using fuzzy computer decision-making are thermostats that respond to a combination of temperature and humidity (comfort factors), an elevator that considers how crowded a car is rather than just its proximity to the desired floor, and a camera that integrates the variables affecting picture quality. These volumes will present a logical progression from implementation and modeling techniques to industrial/commercial applications to fuzzy neural and adaptive fuzzy systems.
Students, research workers, and practitioners in engineering and computer science.
Contents of Volume 1: A. DeGloria, P. Ferrari, D. Grosso, M. Olivieri, and I. Puglisi, Implementation Techniques and Their Applications. H. Ishibuchi and M. Nii, Neural Networks for Fuzzy Rule Approximation. J.V. de Oliviera and J.M. Lemos, Fuzzy System Interface Optimizers in Various Systems Problems. S.H. Nasution, Fuzzy Theory to Critical Path Methods. J. Virant, N. Zimic, and M. Mraz, Fuzzy Sequential Circuits and Automata. K. Hirota and W. Pedrycz, Or/And Neurons in Fuzzy Systems. M. Russo, Hybrid Fuzzy Learning Theory in Systems Modeling. A.J.B. Diz, Fuzzy Systems Based on Petri Net Formalism. W. Pedrycz and J.V. de Oliviera, Optimization Techniques in the Design of Fuzzy Models. W. Pedrycz, Modeling Relationships in Data: From Contingency Tables to Fuzzy Multimodels. Y.-C. Hsu and G. Chen, Fuzzy Dynamical Modeling Techniques for Nonlinear Control Systems and Their Application to Multiple-Input Multiple-Output (MIMO) Systems. E. Deeba, A. de Korvin, and S. Xie, Fuzzy Set Theory to Difference and Functional Equations and Their Utilization in Modeling Diverse Systems. Y. Jin and J. Jiang, Neural Network Based Fuzzy System Identification and Their Application in the Control of Complex Systems. C.-M. Liaw and Y.-S. Kung, Fuzzy Control with Reference Model Following Response. W. Pedrycz, Fuzzy Set Based Models of Neurons and Knowledge-Based Network. L. Wang, R. Langari, and J. Yen, Identifying Fuzzy Rule Based Models Using Orthogonal Transformation and Backpropagation. C.-T. Sun and H.-J. Chiu, Evolutionary Neuro-Fuzzy Modeling. Subject Index.
Contents of Volume 2: A. Aoyama, F.J. Doyle III, and V. Venkatasubramanian, Fuzzy Neural Network Systems for Nonlinear Chemical Process Control Systems. J.L. Koning, Fuzzy Theory in Material Selection for Mechan
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- © Academic Press 1999
- 20th September 1999
- Academic Press
- Hardcover ISBN:
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"The level of these texts makes them suitable for use on senior graduate level courses and the contributors are some of the leading researchers on the subject. . . .this four volume set is very desirable for anyone involved with fuzzy modeling control and signal processing." @source:--MIKE J. GRIMBLE, University of Strathclyde, Glasgow, U.K. @qu:"This valuable compendium . . . most definitely belongs in the libraries of all institutions where teaching or research on fuzzy logic and its applications is conducted." @source:--CHOICE, June 2000 @qu:"As the foreword notes, this valuable compendium of an extensive array of applications of fuzzy set theory emphasizes the practical applications of fuzzy logic, using fuzzy if-then rules. It most definitely belongs in the libraries of all institutions where teaching or research on fuzzy logic and its applications is conducted at any level." @source:--CHOICE, July/August 2000