Please use this identifier to cite or link to this item:
|Type:||Artigo de periódico|
|Title:||Neural approach for solving several types of optimization problems|
|Author:||da Silva, IN|
|Abstract:||Neural networks consist of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural net-works that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its inter-nal parameters are computed explicitly using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the problem considered. The problems that can be treated by the proposed approach include combinatorial optimiza-tion problems, dynamic programming problems, and nonlinear optimization problems.|
|Subject:||recurrent neural networks|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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