Analog neural nonderivative optimizers
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Data
1998-07-01
Autores
Teixeira, MCM
Zak, S. H.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Institute of Electrical and Electronics Engineers (IEEE)
Resumo
Continuous-time neural networks for solving convex nonlinear unconstrained;programming problems without using gradient information of the objective function are proposed and analyzed. Thus, the proposed networks are nonderivative optimizers. First, networks for optimizing objective functions of one variable are discussed. Then, an existing one-dimensional optimizer is analyzed, and a new line search optimizer is proposed. It is shown that the proposed optimizer network is robust in the sense that it has disturbance rejection property. The network can be implemented easily in hardware using standard circuit elements. The one-dimensional net is used as a building block in multidimensional networks for optimizing objective functions of several variables. The multidimensional nets implement a continuous version of the coordinate descent method.
Descrição
Palavras-chave
analog networks, coordinate descent, derivative free optimization, unconstrained optimization
Como citar
IEEE Transactions on Neural Networks. New York: IEEE-Inst Electrical Electronics Engineers Inc., v. 9, n. 4, p. 629-638, 1998.