A fast learning algorithm for neural network applications

Author

Pandya, Abhijit S. ; Szabo, Raisa

Author_Institution

Dept. of Comput. Eng., Florida Atlantic Univ., Boca Raton, FL, USA

fYear

1991

fDate

13-16 Oct 1991

Firstpage

1569

Abstract

Describes the use of the ALOPEX algorithm for solving nonlinear learning tasks by multilayer feedforward networks. ALOPEX is a stochastic parallel process. They demonstrate the use of ALOPEX for modifying the weights in a multilayer perception using a measure of global performance of the network. It estimates the weight changes by using only a scalar cost function which is a measure of global performance. The results of computer simulations of applying ALOPEX to nonrecurrent networks which include any feedforward architecture, in addition to multilayer perceptrons, are presented

Keywords

digital simulation; learning systems; neural nets; parallel algorithms; stochastic processes; ALOPEX; computer simulations; fast learning algorithm; multilayer feedforward networks; multilayer perception; neural network; nonlinear learning tasks; nonrecurrent networks; scalar cost function; stochastic parallel process; weight changes; Application software; Broadcasting; Computer networks; Computer simulation; Cost function; Feedforward systems; Neural networks; Output feedback; Stochastic processes; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on

Conference_Location

Charlottesville, VA

Print_ISBN

0-7803-0233-8

Type

conf

DOI

10.1109/ICSMC.1991.169912

Filename

169912