A time-varying recurrent neural system for convex programming

Author

Wang, Jun

Author_Institution

Dept. of Ind. Technol., North Dakota Univ., Grand Forks, ND, USA

Volume

i

fYear

1991

fDate

8-14 Jul 1991

Firstpage

147

Abstract

The asymptotic stability of a recurrent neural network with monotonically time-varying penalty parameter for optimization is theoretically justified. The conditions of feasibility of solutions generated by the recurrent neural networks are characterized. The conditions of optimality of solutions to convex programming problems generated by the recurrent neural networks are characterized. The design methodology of the operating characteristics of the recurrent neural networks are presented by illustrative examples

Keywords

convex programming; neural nets; stability; time-varying systems; asymptotic stability; convex programming; design methodology; monotonically time-varying penalty parameter; operating characteristics; optimality conditions; optimization; recurrent neural network; solution feasibility conditions; Asymptotic stability; Character generation; Design methodology; Functional programming; Neodymium; Recurrent neural networks; Stability analysis; Sufficient conditions; Time varying systems; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155166

Filename

155166