A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
- 1 March 2001
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 12 (2) , 277-306
- https://doi.org/10.1109/72.914524
Abstract
A new methodology of extraction, optimization, and application of sets of logical rules is described. Neural networks are used for initial rule extraction, local or global minimization procedures for optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. Algorithms for extraction of logical rules from data with real-valued features require determination of linguistic variables or membership functions. Contest-dependent membership functions for crisp and fuzzy linguistic variables are introduced and methods of their determination described. Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables. A tradeoff between accurary/simplicity is explored at the rule extraction stage and between rejection/error level at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Numerous applications of this methodology to benchmark and real-life problems are reported and very simple crisp logical rules for many datasets provided.Keywords
This publication has 33 references indexed in Scilit:
- Survey and critique of techniques for extracting rules from trained artificial neural networksPublished by Elsevier ,2000
- Rule Extraction from Trained Artifical Neural NetworksBehaviormetrika, 1999
- Knowledge-based fuzzy MLP for classification and rule generationIEEE Transactions on Neural Networks, 1997
- Acquiring rule sets as a product of learning in a logical neural architectureIEEE Transactions on Neural Networks, 1997
- Feature space mapping as a universal adaptive systemComputer Physics Communications, 1995
- Knowledge-based artificial neural networksArtificial Intelligence, 1994
- Rule generation from neural networksIEEE Transactions on Systems, Man, and Cybernetics, 1994
- Knowledge-based connectionism for revising domain theoriesIEEE Transactions on Systems, Man, and Cybernetics, 1993
- Functional equivalence between radial basis function networks and fuzzy inference systemsIEEE Transactions on Neural Networks, 1993
- A theory and methodology of inductive learningArtificial Intelligence, 1983