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.
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Damas, M., Salmerón, M., Ortega, J., Olivares, G. (2001). Hybrid Framework for Neuro-Dynamic Programming Application to Water Supply Networks. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_87
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DOI: https://doi.org/10.1007/3-540-45723-2_87
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