Hybrid global learning algorithm based on global search and least squares techniques for backpropagation networks

Chi Tat Leung, Tommy W S Chow

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

16 Citations (Scopus)

Abstract

A hybrid learning algorithm for backpropagation network based on global search and least squares methods is presented to speed up the speed of convergence. The proposed algorithm comprises global search and least squares parts. The global search part trains a backpropagation network over a reduced weight space. The remained weights are calculated in accordance with linear least squares method. Two problems of nonlinear function approximation and modified XOR are applied to demonstrate the fast global search performance of the proposed algorithm. The results indicate that the proposed algorithm enables the learning process to significantly speed up by at most 4670 % in terms of iterations and do not trap in local minima.
Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages1890-1895
Volume3
Publication statusPublished - 1997
Event1997 IEEE International Conference on Neural Networks (ICNN'97) - Westin Galleria Hotel, Houston, Texas, United States
Duration: 9 Jun 199712 Jun 1997

Publication series

Name
Volume3

Conference

Conference1997 IEEE International Conference on Neural Networks (ICNN'97)
Abbreviated titleICNN'97
Country/TerritoryUnited States
CityHouston, Texas
Period9/06/9712/06/97

Fingerprint

Dive into the research topics of 'Hybrid global learning algorithm based on global search and least squares techniques for backpropagation networks'. Together they form a unique fingerprint.

Cite this