Abstract
This paper presents the problem of optimizing a radial basis function neural network from training examples as a multiobjective problem and proposes an evolutionary algorithm to solve it properly. This algorithm incorporates some heuristions the mutation operators to better guide the search towards good solutions. An application to the Mackey-Glass chaotic time series is presented. The prediction accuracy of the proposed method is compared with that of other approaches in terms of the root mean squared error.
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González, J., Rojas, I., Pomares, H., Ortega, J. (2001). RBF Neural Networks, Multiobjective Optimization and Time Series Forecasting. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_59
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DOI: https://doi.org/10.1007/3-540-45720-8_59
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