Study on Nonlinear Multifunctional Sensor Signal Reconstruction Method Based on LS-SVM
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摘要: 提出了基于最小二乘支持向量机(Least squares support vector machine, LS-SVM)的非线性多功能传感器信号重构方法. 不同于通常采用的经验风险最小化重构方法, 支持向量机(Support vector machine, SVM)是基于结构风险最小化准则的新型机器学习方法, 适用于小样本标定数据情况, 可有效抑制过拟合问题并改善泛化性能. 在SVM基础上, LS-SVM将不等式约束转化为等式约束, 极大地简化了二次规划问题的求解. 研究中通过L-折交叉验证实现调整参数优化, 在两种非线性情况下对多功能传感器的输入信号进行了重构, 实验结果显示重构精度分别达到0.154\%和1.146\%, 表明提出的LS-SVM重构方法具有高可靠性和稳定性, 验证了方法的有效性.Abstract: In this paper, the nonlinear multifunctional sensor signal reconstruction method based on the least squares support vector machine (LS-SVM) is proposed. Different from the reconstruction methods with empirical risk minimization, the support vector machine (SVM) is a new machine learning method based on structural risk minimization, which is applicable to the case of small sample size calibration data, and can efficiently restrain overfitting and improve generalization capability. With SVM as a basis, the LS-SVM involves equality constraints instead of inequality constraints, so the solving process of the quadratic programming problem can be greatly simplified. In this study, L-fold cross validation is adopted to optimize the adjustable parameters. The reconstruction of input signals of a multifunctional sensor was carried out in two situations of different nonlinearities for which the reconstruction accuracies were 0.154\% and 1.146\%, respectively. The experimental results demonstrate the high reliability and high stability of the proposed LS-SVM reconstruction method, as well as the feasibility.
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