Use of dynamic tunneling with backpropagation in training feedforward neural networks

Author

Chowdhury, Pinaki Roy ; Singh, Y.P. ; Chansarkar, R.A.

Author_Institution

Defence Terrain Res. Lab., Delhi, India

Volume

1

fYear

1998

fDate

1998

Firstpage

29

Abstract

An algorithm is proposed for supervised training in multilayer feedforward neural networks. The proposed algorithm consists of a gradient descent technique with a dynamic tunneling method for global optimization. Relative to the gradient descent technique like the backpropagation algorithm, the proposed technique gives a faster convergence, and the convergence properties of the proposed algorithm is demonstrated through three problems

Keywords

backpropagation; convergence of numerical methods; feedforward neural nets; gradient methods; optimisation; backpropagation algorithm; convergence properties; dynamic tunneling; feedforward neural networks training; global optimization; gradient descent technique; multilayer feedforward neural networks; supervised training; Backpropagation algorithms; Containers; Convergence; Feedforward neural networks; Intelligent networks; Multi-layer neural network; Neural networks; Optimization methods; Quantum computing; Tunneling;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '98. 1998 IEEE Region 10 International Conference on Global Connectivity in Energy, Computer, Communication and Control

Conference_Location

New Delhi

Print_ISBN

0-7803-4886-9

Type

conf

DOI

10.1109/TENCON.1998.797057

Filename

797057