支持向量机在非线性系统显著误差检测中的应用

L. Nian
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引用次数: 0

摘要

提出了一种基于回归支持向量机的动态数据显著性错误检测的原理和方法。该方法充分利用了支持向量机的非线性逼近能力。建立非线性系统动态过程模型,求解一个二次二次优化问题,可以保证极值解是全局最优解,具有良好的泛化能力。本文以谷氨酸发酵过程为研究对象,建立了基于支持向量机回归的化学和生物变量预测模型。同时实现了工艺变量的在线预测。通过对预测值和实测值的偏差进行打击的方法来确定是否存在显著误差,为动态过程的显著误差检测、消除和修正提供了一种新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Support Vector Machine's Application in Significant Error Detection of Nonlinear Systems
Presented a kind of principle and method based on regression support vector machine dynamic data significant error detection. The method takes full advantage of the nonlinear approximation capability supporting vector machine. The establishment of nonlinear system dynamic process model convex to a quadratic twice optimization problem, which can be guaranteed the extremal solution is global optimal solution and has good generalization ability. In this paper looked glutamic acid fermentation process as the research object, and established the chemical and biological variables prediction model based on SVM regression. At the same time achieved process variables online predicted. Through the method of strike the deviation of predicted value and measured to determine the existence of a significant error, which provide a new method for the significant error detection, eliminate and revise of dynamic process.
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