Research of load identification based on multiple-input multiple-output SVM model selection

Abstract

In this article, the problem of multiple-input multiple-output (MIMO) load identification is addressed. First, load identification is proved in dynamic theory as non-linear MIMO black-box modelling process. Second, considering the effect of hyper-parameters in small-size sample problem, a new MIMO Support Vector Machine (SVM) model selection method based on multi-objective particle swarm optimization is proposed in order to improve the identification's performance. The proposed method treats the model selection of MIMO SVM as a multi-objective optimization problem, and leave-one-out generalization errors of all output models are minimized simultaneously. Once the Pareto-optimal solutions are found, the SVM model with the best generalization ability is determined. The proposed method is evaluated in the experiment of dynamic load identification on cylinder stochastic vibration system, demonstrating its benefits in comparison to the existing model selection methods in terms of identification accuracy and numerical stability, especially near the peaks.