Abstract
A hybrid method, based on evolutionary computation, Monte Carlo simulation, and neural networks for functional approximation and time series prediction, is proposed to reduce the high computational cost usually required by dynamic programming problems, that appear in complex real applications. As an example of application a scheduling problem related with the control of a water supply network is considered. © Springer-Verlag Berlin Heidelberg 2001.
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CITATION STYLE
Damas, M., Salmerón, M., Ortega, J., & Olivares, G. (2001). Hybrid framework for neuro-dynamic programming application to water supply networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2085 LNCS, pp. 719–727). Springer Verlag. https://doi.org/10.1007/3-540-45723-2_87
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