JOURNAL ARTICLE

NEW METHODOLOGY TO FUZZY REASONING FOR RULE-BASED EXPERT SYSTEMS

Shyi‐Ming Chen

Year: 1995 Journal:   Cybernetics & Systems Vol: 26 (2)Pages: 265-266   Publisher: Taylor & Francis

Abstract

Abstract Much knowledge residing in the knowledge base of an expert system involves fuzzy concepts. A powerful expert system must have the capability of fuzzy reasoning. This paper presents a new methodology for dealing with fuzzy reasoning based on the matching function S. The single-input, single-output (SISO) fuzzy reasoning scheme and the multi-input, single-output (MISO) fuzzy reasoning schemes are discussed in detail. The proposed fuzzy reasoning methodology is conceptually clearer than the compositional rule of inference approach. It can provide an useful way for rule-based systems to deal with fuzzy reasoning.

Keywords:
Computer science Neuro-fuzzy Artificial intelligence Model-based reasoning Legal expert system Expert system Reasoning system Adaptive neuro fuzzy inference system Fuzzy set operations Opportunistic reasoning Fuzzy rule Fuzzy logic Fuzzy classification Knowledge base Defuzzification Fuzzy control system Fuzzy number Fuzzy set Knowledge representation and reasoning

Metrics

6
Cited By
0.55
FWCI (Field Weighted Citation Impact)
0
Refs
0.71
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Fuzzy Logic and Control Systems
Physical Sciences →  Computer Science →  Artificial Intelligence
Multi-Criteria Decision Making
Social Sciences →  Decision Sciences →  Management Science and Operations Research
Neural Networks and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence

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