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Fuzzy multivariate rule‐building expert systems: Minimal neural networks
Author(s) -
Harrington Peter B.
Publication year - 1991
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180050506
Subject(s) - artificial neural network , artificial intelligence , data mining , computer science , fuzzy rule , neuro fuzzy , expert system , fuzzy logic , adaptive neuro fuzzy inference system , robustness (evolution) , fuzzy classification , machine learning , fuzzy control system , pattern recognition (psychology) , biochemistry , chemistry , gene
A fuzzy multivariate rule‐building expert system (FuRES) has been devised which also functions as a minimal neural network. This system builds rules from training sets of data that use feature transformation in their antecedents. The rules are constructed using the ID3 algorithm with a fuzzy expression of classification entropy. The rules are optimal with respect to fuzziness and can accommodate overlapped and underlapped clusters of data. The FuRES algorithm combines the benefits obtained from simulated annealing and gradient optimization, which provide robustness and efficiency respectively. FuRES classification trees support OR logic in their inference. The system automatically generates meaningful and consistent certainty factors during rule construction. Unlike other neural networks, FuRES uses local processing which furnishes qualitative information in the rule structure of its classification trees and variable loadings of the weight vectors.