Summary
The learning process of multilayer neural networks is considered as a multistage optimal control process. For small values of the gain parameter of used neurons for the learning differential dynamic programming of first order can be applied. Adjustment of the gain parameters can be done by continuation methodology as was described in [1]. In this paper by considering the gain parameter as an additional control variableā starting form a small value of the parameter, the optimal value of the parameter is found. The methodology we propose to call the heuristic dynamic programming.
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References
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Krawczak, M. (2003). Heuristic Dynamic Programming for Neural Networks Learning Part 2: I-order Differential Dynamic Programming. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_31
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_31
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