Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling
- 12 October 2005
- journal article
- Published by Springer Nature in Soft Computing
- Vol. 10 (9) , 717-734
Abstract
No abstract availableKeywords
This publication has 46 references indexed in Scilit:
- Linguistic modeling with weighted double-consequent fuzzy rules based on cooperative coevolutionary learningIntegrated Computer-Aided Engineering, 2003
- Linguistic modeling with hierarchical systems of weighted linguistic rulesInternational Journal of Approximate Reasoning, 2003
- Applying Rule Weight Derivation to Obtain Cooperative Rules2003
- Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy ModelsLecture Notes in Computer Science, 2003
- COR: a methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rulesIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2002
- A proposal for improving the accuracy of linguistic modelingIEEE Transactions on Fuzzy Systems, 2000
- A highly interpretable form of Sugeno inference systemsIEEE Transactions on Fuzzy Systems, 1999
- Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis TechniquesApplied Intelligence, 1999
- A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examplesInternational Journal of Approximate Reasoning, 1997
- HOW TO HANDLE THE FLEXIBILITY OF LINGUISTIC VARIABLES WITH APPLICATIONSInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1994