Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)
Cluster Analysis for Data Mining and System Identification
Cluster Analysis for Data Mining and System Identification
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
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The problem of increasing of the quality, of the automation level and the synthesis rate of neuro-fuzzy network (NFN) has been solved in the paper. The method of neuro-fuzzy network synthesis and simplification on precedents has been firstly proposed. It is based on the using of the feature space pseudo-clustering, on the automatic formation of fuzzy terms and rules, on the automatic NFN structure and parameter synthesis by the training set, and on the reducing of NFN structural and parametric complexity by simplifying the rules and reducing the number of redundant terms. This can increase the speed of NFN construction, enhance its properties and generalize interpretability. The proposed method has been implemented in the developed software and was used for the practical problem solving of technical diagnosis.