Interpolation of fuzzy if-then rules by neural networks

DOI10.1016/0888-613X(94)90006-XzbMath0794.68133OpenAlexW2020889369MaRDI QIDQ1317380

Hideo Tanaka, Hidehiko Okada, Hisao Ishibuchi

Publication date: 24 March 1994

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

Full work available at URL: https://doi.org/10.1016/0888-613x(94)90006-x

zbMATH Keywords

back-propagationfuzzy if-then ruleslearning of neural networkstrainable multilayer feedforward neural networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy sets and logic (in connection with information, communication, or circuits theory) (94D05)

Related Items (8)

Learning by fuzzified neural networks ⋮ Neurofuzzy mathematical model for monitoring flow parameters of natural gas. ⋮ RSPOP: Rough Set–Based Pseudo Outer-Product Fuzzy Rule Identification Algorithm ⋮ Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks ⋮ Numerical analysis of the learning of fuzzified neural networks from fuzzy if-then rules ⋮ THE FUZZY METRIC-TRUTH REASONING APPROACH TO DECISION MAKING IN SOFT COMPUTING MILIEUX ⋮ Multi-Objective Optimization and Cluster-Wise Regression Analysis to Establish Input–Output Relationships of a Process ⋮ Fuzzified neural network based on fuzzy number operations

Cites Work

• Unnamed Item
• An introductory survey of fuzzy control
• Implementation of conjunctive and disjunctive fuzzy logic rules with neural networks
• The concept of a linguistic variable and its application to approximate reasoning. III
• Fuzzy logic in control systems: fuzzy logic controller. II

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