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 identifi-cation 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-objec-tive 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 experi-ment of dynamic load identification on cylinder stochastic vibration system, demonstrating its benefits in comparison to the existing model selection methods in terms of identification accu-racy and numerical stability, especially near the peaks