Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
Rule extraction for fuzzy modeling
Fuzzy Sets and Systems
Rule extraction from trained neural networks using genetic algorithms
Proceedings of the second world congress on Nonlinear analysts: part 3
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue on clustering and learning
Interpretation of Trained Neural Networks by Rule Extraction
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Fuzzy CoCo: a cooperative-coevolutionary approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
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In this paper, the REX method of fuzzy rule extraction from neural networks (NN) is presented. It is based on evolutionary algorithms. In the search process of the evolutionary algorithm, a set of rules describing the performance of the NN is found. An evolutionary algorithm is also responsible for obtaining proper fuzzy sets. Two approaches are compared, namely REX Pitt and REX Michigan. The main difference lies in the information contained in one chromosome. In REX Pitt, one individual represents a set of rules, while in REX Michigan it represents one rule. The obtained results are compared to other known methods. REX Pitt has very good efficiency, producing a small number of fuzzy rules, while REX Michigan creates more low quality rules.