A resource-allocating network for function interpolation
Neural Computation
An efficient MDL-based construction of RBF networks
Neural Networks
Evolving Multilayer Perceptrons
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
RBF Neural Networks, Multiobjective Optimization and Time Series Forecasting
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
Expert Mutation Operators for the Evolution of Radial Basis Function Neural Networks
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
Selected Papers from AISB Workshop on Evolutionary Computing
Fast learning in networks of locally-tuned processing units
Neural Computation
Regularization in the selection of radial basis function centers
Neural Computation
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
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An evolutionary algorithm with specific operators has been developed to automatically find Radial basis Functions Neural Networks that solve a given problem. The evolutionay algorithm optimizes all the parameters related to the neural network architecture, i.e., number of hidden neurons and their configuration. A set of parameters to run the algorithm is found and tested against a set of different problems about Time-series forecasting and function approximation. Results obtained are compared with those yielded by similar methods.