支持向量机在焊接过程中的应用  

Review of support vector machine and its applications in welding process

作　　者：李驰[1] 

机构地区：[1]四川大学锦城学院计算机科学与软件工程系,四川成都611731

出　　处：《电焊机》2011年第10期42-45,共4页Electric Welding Machine

摘　　要：焊接过程是典型的非线性系统,涉及到复杂的物理、化学过程,基于神经网络的建模方法往往不能有效地获得系统模型。支持向量机在解决小样本、非线性和高维的机器学习问题中表现出许多特有的优势,非常适合于复杂的非线性系统建模,因此在焊接过程系统建模中具有广阔的应用前景。介绍了支持向量机的基本理论,给出了支持向量机在焊接过程中的系统建模方法,重点综述了支持向量机在焊接中的应用,包括在焊接过程建模与控制、焊接状态模式识别、焊接质量检测和焊接工业参数识别与优化中的应用。分析了支持向量机在焊接应用过程中所面临的问题。Welding process is a typical nonlinear system,involving complex physical and chemical processes,but modeling methods often can not effectively access the system model based on neural networks.Support vector machine（SVM）manifests unique advantages in solving machine learning problem that is characterized by small quantity of samples,nonlinear and high dimension space,and SVM manifests unique advantages in modeling of nonlinear system,so it has broad application prospect in welding.First of all,the basic theory of SVM is introduced and modeling mothed is given in welding process.An overview of SVM is given in welding applications, involving welding process modeling and control ,welding state pattern recognition, welding quality control and parameter identification and optimization of the welding industry at the same time.The problems faced in the process of welding is analyzed at last.

关 键 词：支持向量机 机器学习 焊接 

分 类 号：TG409[金属学及工艺—焊接]