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Neural Network Based Algorithm for Dynamic System Optimization
Author(s) -
Romero Roseli Francelin,
Kacprzyk Janusz,
Gomide Fernando
Publication year - 2001
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1111/j.1934-6093.2001.tb00052.x
Subject(s) - artificial neural network , computer science , shortest path problem , nonlinear system , dynamic programming , class (philosophy) , path (computing) , nonlinear programming , mathematical optimization , algorithm , artificial intelligence , mathematics , theoretical computer science , graph , physics , quantum mechanics , programming language
A class of artificial neural networks with a two‐layer feedback topology to solve nonlinear discrete dynamic optimization problems is developed. Generalized recurrent neuron models are introduced. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. A comparative analysis of the computational requirements is made. The analysis shows advantages of this approach as compared to the standard dynamic programming algorithm. The technique has been applied to several important optimization problems, such as shortest path and control optimal problems.