An Improved Neural Network Algorithm and its Application in Sinter Cost Prediction

被引：6

作者：

Wang, Bin ^{[1
]}

Yang, Bin ^{[1
]}

Sheng, Jinfang ^{[1
]}

Chen, Mengsheng ^{[2
]}

He, Guoqiang ^{[2
]}

机构：

[1] Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China

[2] Changtian Int Engn CO Ltd MCC, Automat Dept, Changsha, Hunan, Peoples R China

来源：

WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS | 2009年

关键词：

Neural network; line search; conjugate gradient; convergence; sinter cost;

D O I：

10.1109/WKDD.2009.180

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies various training algorithms of BP neural network and proposes an improved conjugate gradient algorithm which combines conjugate gradient algorithm with inexact line search route based on generalized Curry principle. The proposed algorithm has global convergence, optimizes the learning steps using new line search rules and improves the convergence speed. The new algorithm is applied in the cost prediction of actual sintering production. Simulation results show that the algorithm has better convergence compared with traditional conjugate gradient algorithms. The MSE of prediction is 0.0098 and accuracy rate reaches 94.31%.

引用

页码：112 / +

页数：2

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