大连理工大学  登录  English 
刘巍
点赞:

教授   博士生导师   硕士生导师

主要任职: 机械工程学院院长、党委副书记

性别: 男

毕业院校: 大连理工大学

学位: 博士

所在单位: 机械工程学院

学科: 机械电子工程. 测试计量技术及仪器. 精密仪器及机械

办公地点: 辽宁省大连市大连理工大学机械工程学院知方楼5027

联系方式: 辽宁省大连市大连理工大学机械工程学院,116023

电子邮箱: lw2007@dlut.edu.cn

手机版

访问量:

开通时间: ..

最后更新时间: ..

Hybrid of simulated annealing and SVM for hydraulic valve characteristics prediction

点击次数:

论文类型: 期刊论文

发表时间: 2011-07-01

发表刊物: EXPERT SYSTEMS WITH APPLICATIONS

收录刊物: SCIE、EI

卷号: 38

期号: 7

页面范围: 8030-8036

ISSN号: 0957-4174

关键字: Characteristics prediction; Simulated annealing; SVM; Hydraulic valve

摘要: Accurate prediction for the synthesis characteristics of hydraulic valve in industrial production plays an important role in decreasing the repair rate and the reject rate of the product. Recently, Support Vector Machine (SVM) as a highly effective mean of system modeling has been widely used for predicting. However, the important problem is how to choose the reasonable input parameters for SVM. In this paper, a hybrid prediction method (SA-SVM for short) is proposed by using simulated annealing (SA) and SVM to predict synthesis characteristics of the hydraulic valve, where SA is used to optimize the input parameters of SVM based prediction model. To validate the proposed prediction method, a specific hydraulic valve production is selected as a case study. The prediction results show that the proposed prediction method is applicable to forecast the synthesis characteristics of hydraulic valve and with higher accuracy. Comparing with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN) are also made. (C) 2010 Elsevier Ltd. All rights reserved.

辽ICP备05001357号 地址:中国·辽宁省大连市甘井子区凌工路2号 邮编:116024
版权所有:大连理工大学