Study on Nonlinear Multifunctional Sensor Signal Reconstruction Method Based on LS-SVM
基于LS-SVM的非线性多功能传感器信号重构方法研究

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.