A weight evolution algorithm with deterministic perturbation

Author

Ng, Sin-Chun ; Leung, Shu-hung ; Luk, Andrew

Author_Institution

Dept. of Comput. & Math, Hong Kong Inst. of Vocational Educ., China

Volume

1

fYear

2000

fDate

2000

Firstpage

185

Abstract

Introduces a learning algorithm for multi-layered feedforward network-weight evolution algorithm with deterministic perturbation. During the learning phase of the gradient algorithm (such as backpropagation), the network weights are adjusted intentionally in order to have an improvement in system performance. The intention is to reduce the overall system error after every weight update. By looking at the error component, it is possible to adjust some of the network weights dramatically so as to have an overall reduction in system error. Using the deterministic perturbation, it is found that the weight evolution between the hidden and output layer can accelerate the convergence speed, whereas the weight evolution between the input layer and the hidden layer can assist in solving the local minima problem

Keywords

convergence; feedforward neural nets; gradient methods; learning (artificial intelligence); multilayer perceptrons; convergence speed; deterministic perturbation; gradient algorithm; hidden layer; learning algorithm; local minima problem; multi-layered feedforward network; network weights; output layer; weight evolution algorithm; Acceleration; Australia; Convergence; Equations; Error correction; Investments; Mathematics; System performance;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.857834

Filename

857834