Genetic algorithms + data structures = evolution programs (3rd ed.)
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Evolutionary optimization of RBF networks
Radial basis function networks 1
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Optimizing RBF Networks with Cooperative/Competitive Evolution of Units and Fuzzy Rules
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Evolutionary Optimization of RBF Networks
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
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SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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ARCS'07 Proceedings of the 20th international conference on Architecture of computing systems
Fast and efficient training of RBF networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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Information Sciences: an International Journal
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This article deals with two key problems of data mining, the automation of the data mining process and the integration of human domain experts. We show how an evolutionary algorithm (EA) can be used to optimize radial basis function (RBF) neural networks used for classification tasks. First, input features will be chosen from a set of possible input features (feature selection). Second, the number of hidden neurons is adapted (model selection). It is known that interpretable (fuzzy-type) rule sets may be extracted from RBF networks. We show how appropriate training algorithms for RBF networks and penalty terms in the fitness function of the EA may improve the understandability of the extracted rules. The properties of our approach are set out by means of two industrial application examples (process identification and quality control).