A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples.

DOI10.1016/S0888-613X(96)00133-8zbMath1078.93541OpenAlexW2129880717WikidataQ62608516 ScholiaQ62608516MaRDI QIDQ1125756

Francisco Herrera, Oscar Cordón

Publication date: 1997

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0888-613x(96)00133-8

zbMATH Keywords

genetic algorithmsinductive learningevolution strategiesnichingfuzzy logic controllersfuzzy-logic-controller knowledge base

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42) Reasoning under uncertainty in the context of artificial intelligence (68T37) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

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Uses Software

• Genocop

Cites Work

• Tuning fuzzy logic controllers by genetic algorithms
• Applicability of the fuzzy operators in the design of fuzzy logic controllers
• Genetic algorithms and soft computing
• Fuzzy logic in control systems: fuzzy logic controller. II
• An experiment in linguistic synthesis with a fuzzy logic controller
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