Fuzzy Neural Model for Flatness Pattern Recognition

被引：22

作者：

Jia Chun-yu ^{[1
]}

Shan Xiu-ying ^{[1
]}

Liu Long-min ^{[1
]}

Niu Zhao-ping ^{[1
]}

机构：

[1] Yanshan Univ, Sch Mech Engn, Qinhuangdao 066004, Hebei, Peoples R China

来源：

JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL | 2008年 / 15卷 / 06期

基金：

中国国家自然科学基金;

关键词：

flatness; pattern recognition; Legendre orthodoxy polynomial; genetic-BP algorithm; fuzzy neural network;

D O I：

10.1016/S1006-706X(08)60262-9

中图分类号：

TF [冶金工业];

学科分类号：

0806 [冶金工程];

摘要：

For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based oil fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti-interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient. practical. and novel method for flatness pattern recognition.

引用

页码：33 / 38

页数：6