A neural network model based on differential-algebraic equations for nonlinear programming

Author

Xiong, Momiao ; Arnold, Jonathan ; Chen, Hubert J.

Author_Institution

Georgia Univ., Athens, GA, USA

fYear

1993

fDate

1993

Firstpage

923

Abstract

A neural network model based on differential-algebraic equations for nonlinear programming is proposed. The penalty function method or barrier function method is used to convert a constrained optimization problem into a single unconstrained optimization problem by placing the constraints into the objective function. The resulting nonsmooth unconstrained penalty problem or barrier problem for finding an optimal penalty or barrier parameters is solved by a differential inclusion. A method for selecting a single or valued vector-field is presented. The global and local convergence properties of the new neural network model for nonlinear programming are analyzed. Examples are used to demonstrate that the network is both fast and more accurate than that of previous neural network models and classical methods

Keywords

convergence; neural nets; nonlinear programming; barrier function method; barrier parameters; constrained optimization problem; differential inclusion; differential-algebraic equations; global convergence; local convergence; neural network model; nonlinear programming; nonsmooth unconstrained penalty problem; objective function; penalty function method; valued vector-field; Constraint optimization; Differential algebraic equations; Differential equations; Neural networks; Nonlinear equations; Optimization methods; Stochastic systems; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993., IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0999-5

Type

conf

DOI

10.1109/ICNN.1993.298681

Filename

298681