| 基于支持向量机的测厚仪CS值电压漂移故障判定及处理 |
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| 引用本文：吴烜,李京.基于支持向量机的测厚仪CS值电压漂移故障判定及处理[J].计算技术与自动化,2014,(1):42-45 |
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| 中文摘要:支持向量机(SVM)是在统计学习理论基础上发展起来的一种新的模式识别方法， SVM的基本思想是通过非线性变换将输入空间变换到一个高维空间，然后在这个新的空间中求取最优分类超平面。它在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。本文着重介绍选取SVM及其如何成功诊断处理钢厂轧机X射线测厚仪CS值电压自动漂移等故障的实例，实践理论与应用并重。 |
| 中文关键词:支持向量机 测厚仪 电压漂移 |
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| Thickness Gauge CS Value Voltage Drift Down Approach Based on Support Vector Machines Algorithm Determines |
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| Abstract:Support vector machines ( SVM ) is a new pattern recognition method developed on the basis of statistical learning theory, the basic idea of SVM is to input data into a high dimensional space by a nonlinear transformation, then the optimal separating hyper plane in this new space. It has many advantages in solving small sample, nonlinear and high dimensional pattern recognition problem, and can be applied to the function fitting to other machine learning problems. This paper focuses on the selection of SVM and how to successfully diagnosis example related processing steel mill X ray thickness gauge CS value automatic voltage drift failure, theory of practice and application. |
| keywords:Support Vector Machines ( SVM ) thickness gauge voltage drift |
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