A fast hybrid algorithm of global optimization for feedforward neural networks

被引：0

作者：

Jiang, MH ^{[1
]}

Zhu, XY ^{[1
]}

Yuan, BZ ^{[1
]}

Tang, XF ^{[1
]}

Lin, BQ ^{[1
]}

Ruan, QQ ^{[1
]}

Jiang, MY ^{[1
]}

机构：

[1] No Jiaotong Univ, Inst Sci Informat, Beijing 100044, Peoples R China

来源：

2000 5TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS, VOLS I-III | 2000年

关键词：

the conjugate gradient algorithm; global convergence; backpropagation; inexact line search;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN). The effect of inexact line search on conjugacy was studied and a generalized conjugate gradient method based on this effect was proposed and shown to have global convergence for error backpagation of MLFNN. The descent property and global convergence was given for the improved hybrid algrithm of conjugate gradient algorithm, the results of the proposed algorithm show a considerable improvement over the Fletcher-Rreeves algorithm and conventional BP algorithm, it overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithm that maybe plung into local minima.

引用

页码：1609 / 1612

页数：4

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