基于LS-SVM的非线性多功能传感器信号重构方法研究

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.

Study on Nonlinear Multifunctional Sensor Signal Reconstruction Method Based on LS-SVM

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 minimiza- tion,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 general- ization 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 %,respec- tively.The experimental results demonstrate the high reliability and high stability of the proposed LS-SVM reconstruction method,as well as the feasibility.