Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
The approximation of piecewise linear membership functions and Łukasiewicz operators
Fuzzy Sets and Systems
Information Sciences: an International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
Preknowledge-based generalized association rules mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper we are dealing with the construction of a fuzzy rule based classifier. A three-step method is proposed based on Łukasiewicz logic for the description of the rules and the fuzzy memberships to construct concise and highly comprehensible fuzzy rules. In our method, a genetic algorithm is applied to evolve the structure of the rules and then a gradient based optimization to fine tune the fuzzy membership functions. The introduced squashing function allows us not only to handle the approximation of the operators and the memberships in the same way, but also to efficiently calculate the derivatives of the membership functions. We also show applications of the model on the UCI machine learning database.