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Crisp and Soft Computing with Hypercubical Calculus: New Approaches to Modeling in Cognitive Science and Technology with Parity Logic, Fuzzy Logic, ... (Studies in Fuzziness and Soft Computing, 27) Hardback - 1999 - 1999th Edition
by Zaus, Michael
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- Hardback
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Details
- Title Crisp and Soft Computing with Hypercubical Calculus: New Approaches to Modeling in Cognitive Science and Technology with Parity Logic, Fuzzy Logic, ... (Studies in Fuzziness and Soft Computing, 27)
- Author Zaus, Michael
- Binding Hardback
- Edition number 1999th
- Edition 1999
- Pages 425
- Volumes 1
- Language ENG
- Publisher Physica
- Publication date 1999
- Illustrated Yes
- Features Illustrated, Maps
- Bookseller's Inventory # ZB1347983
- ISBN 9783790811728 / 3790811726
- Weight 1.75 lbs (0.79 kg)
- Dimensions 9.21 x 6.14 x 1 in (23.39 x 15.60 x 2.54 cm)
- Category Computers - General Information
- Library of Congress Catalogue Number 98053207
- Dewey Decimal Code 006.3
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From the publisher
First line
This chapter previews the underlying approaches comprising parity logic, fuzzy logic, and evolutionary computing.
From the rear cover
Three transdisciplinary mainstreams of crisp and soft computing are presented in this book. (1) An entirely new approach to scientific modeling from scratch as based on parity logic with new operators for binary computing and the new framework of Langlet transforms. (2) A compact overview of the foundations of fuzzy logic, and a comprehensive treatment of fuzzy nonlinear dynamical predictor systems in terms of fuzzy cognitive maps. Readers interested in new ways of causal modeling and nonlinear forecasting are introduced to fuzzy knowledge engineering as a paradigm shift in intelligent computing. (3) New perspectives for evolutionary computing with an integro-differential operator from parity logic, and a systematic elaboration of autogenetic algorithms for search in high dimensional feature spaces. Readers interested in fast computing, practical applications of causal reasoning with fuzzy logic, and interactive experimental control environments as based on evolutionary computing, will gain significant insights into a variety of computational power tools.
