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1 Mar 1994 Full Text: PDF . Chin-Teng Lin , C. S. George Lee, Neural-Network-Based Fuzzy Logic Control and Decision System, IEEE Transactions on Computers, v.40 n.12, p.1320-1336, December 1991 . Lotfi A. Zadeh, Soft Computing and Fuzzy Logic, IEEE Software, v.11 n.6, p.48-56, November 1994.
Soft computing is a new, emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. The principal components of soft computing are fuzzy logic, neurocomputing, genetic algorithms and probabilistic reasoning.
Contents. • Review of classical logic and reasoning systems. • Fuzzy sets. • Fuzzy logic. • Fuzzy logic systems applications. • Fuzzy Logic Minimization and Synthesis. • Practical Examples. • Approaches to fuzzy logic decomposition. • Our approach to decomposition. • Combining methods and future research
BOOKS. [1]. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing", PHI, 2004, Pearson. Education 2004. [2]. Timothy J.Ross, “Fuzzy Logic with Engineering Applications", McGraw-Hill, International. Editions, Electrical Engineering Series, Singapore, 1997. [3]. Davis E.Goldberg, “Genetic Algorithms: Search
Abstract. We survey on the theoretical and practical developments of the theory of fuzzy logic and soft computing. Specifically, we briefly re- view the history and main milestones of fuzzy logic (in the wide sense), the more recent development of soft computing, and finalise by present- ing a panoramic view of applications:
Lecture Notice "Introduction to Soft Computing" are based on Heikki Koivo "Soft Computing in. Dynamical Systems" and Robert Fuller "Introduction to Neuro-Fuzzy Systems" books. Fuzzy logic systems chapter describes the basic definitions of fuzzy set theory, i.e., the basic notions, the properties of fuzzy sets and operations
4 Nov 1994 computing, and probabilistic rea- soning. Soft computing is likely to play an increasingly impor- tant role in many application areas, including sof2ware engi- neering. The role modelfor SOB computing is the human mind. Lomputing. 1 ;r' and Fuzzy. Logic. LOTFI A. ZADEH, University of California at Berkeley.
Chapter 1: Introduction. 1. A Genetic-Fuzzy Approach to Measure Multiple Intelligence. Chapter 1: Introduction. Evolutionary. Computation. • Soft Computing. • Genetic Algorithm. • Fuzzy Logic. Application. Domain. • Theory of MultipIe. Intelligence. Intelligent. System. Framework. • Objectives of Research. • Flow of Thesis
Soft Computing: Neural Networks and Fuzzy Logic. Emil M. Petriu, Dr. Eng., FIEEE. School of Information Technology and Engineering. University of Ottawa. Ottawa, ON., K1N 6N5 Canada www.site.uottawa.ca/~petriu
19 Dec 2017 Full-text (PDF) | We survey on the theoretical and practical developments of the theory of fuzzy logic and soft computing. Specically, we briey re- view the history and main milestones of fuzzy logic (in the wide sense), the more recent development of soft computing, and nalise by present- ing a p
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