Publicação: Analog neural nonderivative optimizers
Carregando...
Data
Autores
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Institute of Electrical and Electronics Engineers (IEEE)
Tipo
Artigo
Direito de acesso
Acesso restrito
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
Idioma
Inglês
Como citar
IEEE Transactions on Neural Networks. New York: IEEE-Inst Electrical Electronics Engineers Inc., v. 9, n. 4, p. 629-638, 1998.
