Extraction of Fuzzy Rules Using Fuzzy Neural Networks with Forgetting

FRULEX - Fuzzy Rules Extraction Using Rapid Back Propagation Neural Networks

In this paper, we present a new approach for extracting fuzzy rules from numerical input–output data for pattern classification. The approach combines the merits of the fuzzy logic theory, and neural networks. The proposed approach uses rapid back propagation neural network (RBPNN), which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The network can be regarde...

INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

Extraction of OR fuzzy rules from Artificial Neural Networks

A fuzzy rule based system equivalent to an ANN is presented in this work. The inputs of this system are the input variables of the ANN and it uses fuzzy unions in the if-part of their fuzzy rules. Thus, the knowledge inside this system is comprehensible.

The extraction of Sugeno fuzzy rules from neural networks

Extraction of similarity based fuzzy rules from artificial neural networks

A method to extract a fuzzy rule based system from a trained artificial neural network for classification is presented. The fuzzy system obtained is equivalent to the corresponding neural network. In the antecedents of the fuzzy rules, it uses the similarity between the input datum and the weight vectors. This implies rules highly understandable. Thus, both the fuzzy system and a simple analysi...