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Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki, 2007, Volume 47, Number 8, Pages 1428–1454
(Mi zvmmf271)
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This article is cited in 2 scientific papers (total in 2 papers)
Knowledge representation and acquisition in expert systems for pattern recognition
O. M. Vasil'ev, D. P. Vetrov, D. A. Kropotov Faculty of Computational Mathematics and Cybernetics, Moscow State University, Leninskie gory, Moscow, 119992, Russia
Abstract:
A new approach to the design of fuzzy expert systems is proposed. The representation of knowledge and the formation of statements by fuzzy logic tools are discussed in detail. A model of fuzzy inference is described. Primary attention is given to automatic extraction of knowledge (fuzzy inference rules) from a set of precedents. Various performance criteria for rules are introduced, and an algorithm for their generation (the method of effective restrictions) is proposed. An extension of the type of admissible rules by introducing a fuzzy disjunction operation is described. The possibility of optimizing the rules found is explored. The benefits of the approaches proposed are illustrated by experiments.
Key words:
pattern recognition, data mining, fuzzy logic, expert systems.
Received: 01.09.2006 Revised: 21.02.2007
Citation:
O. M. Vasil'ev, D. P. Vetrov, D. A. Kropotov, “Knowledge representation and acquisition in expert systems for pattern recognition”, Zh. Vychisl. Mat. Mat. Fiz., 47:8 (2007), 1428–1454; Comput. Math. Math. Phys., 47:8 (2007), 1373–1397
Linking options:
https://www.mathnet.ru/eng/zvmmf271 https://www.mathnet.ru/eng/zvmmf/v47/i8/p1428
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