基于支持向量机校正虚拟仪器非线性误差研究

Research on Calibration of Nonlinear Error of Virtual Instrument Based on Support Vector Machine

支持向量机是近几年发展起来的机器学习的新方法,它较好地解决了小样本、非线性、高维数、局部极小点等实际问题。本文讨论了基于支持向量机实现虚拟仪器系统非线性校正的原理和方法,并将该方法应用于一浓度测量虚拟仪器,取得了满意的效果。实践结果表明,该方法有很好的应用前景和研究价值。

Support vector machine （SVM） is a novel machine learning method, which is powerful for the problem characterized by small sample, nonlinearity, high dimension and local minima. In this paper, the principle and method based on support vector machine of nonlinear calibration in virtual instrument system are discussed. When this method is applied to a instrument system of measurement concentration, satisfactory result is obtained, which indicates it has very good application prospect and research value.