An adaptive conjugate gradient learning algorithm for efficient training of neural networks

H. Adeli*, Shih-Lin Hung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

112 Scopus citations

Abstract

An adaptive conjugate gradient learning algorithm has been developed for training of multilayer feedforward neural networks. The problem of arbitrary trial-and-error selection of the learning and momentum ratios encountered in the momentum backpropagation algorithm is circumvented in the new adaptive algorithm. Instead of constant learning and momentum ratios, the step length in the inexact line search is adapted during the learning process through a mathematical approach. Thus, the new adaptive algorithm provides a more solid mathematical foundation for neural network learning. The algorithm has been implemented in C on a SUN-SPARCstation and applied to two different domains: engineering design and image recognition. It is shown that the adaptive neural networks algorithm has superior convergence property compared with the momentum backpropagation algorithm.

Original languageEnglish
Pages (from-to)81-102
Number of pages22
JournalApplied Mathematics and Computation
Volume62
Issue number1
DOIs
StatePublished - 1 Jan 1994

Fingerprint Dive into the research topics of 'An adaptive conjugate gradient learning algorithm for efficient training of neural networks'. Together they form a unique fingerprint.

Cite this