Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems

DOI10.1016/S0165-0114(97)00105-XzbMath0942.68110OpenAlexW1993628150MaRDI QIDQ1302235

Jose Jesus Castro-Schez, Jose Manuel Zurita, Juan Luis Castro

Publication date: 23 August 2000

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0165-0114(97)00105-x

zbMATH Keywords

machine learningclassification problems

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy logic; logic of vagueness (03B52) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35)

Related Items (7)

A fuzzy c-means variant for the generation of fuzzy term sets. ⋮ Learning maximal structure fuzzy rules with exceptions ⋮ Sensitivity analysis of fuzzy rule-based classification systems by means of the Lipschitz condition ⋮ A supervised learning approach to automate the acquisition of knowledge in surveillance systems ⋮ GENERATING FUZZY RULES FROM TRAINING DATA CONTAINING NOISE FOR HANDLING CLASSIFICATION PROBLEMS ⋮ Use of a fuzzy machine learning technique in the knowledge acquisition process ⋮ A NEW METHOD TO CONSTRUCT MEMBERSHIP FUNCTIONS AND GENERATE WEIGHTED FUZZY RULES FROM TRAINING INSTANCES

Cites Work

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• Automatic learning of fuzzy naming relations over finite languages
• Successive identification of a fuzzy model and its applications to prediction of a complex system
• Learning rules for a fuzzy inference model
• Learning maximal structure fuzzy rules with exceptions
• A learning methodology in uncertain and imprecise environments

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