Parameter Estimation of Noise-Disturbed Multivariate Systems Using Support Vector Regression Integrated with Random Search and Bayesian Optimization

To achieve accurate control of Multi-Input and Multi-Output (MIMO) physical plants, it is crucial to obtain correct model expressions. In practice, the prevalence of both outliers and colored noise can cause serious interference with the industrial process, thus reducing the accuracy of the identification algorithm. The algorithm of support vector regression (SVR) is proposed to address the problem of parameter estimation for MIMO systems under interference from outliers and colored noise. In order to further improve the speed of parameter estimation, random search and Bayesian optimization algorithms were introduced, and the support vector regression combining stochastic search and Bayesian optimization (RSBO-SVR) algorithm was proposed. It was verified by simulation and tank experiments. The results showed that the method has strong anti-interference ability and can achieve high-precision parameter identification. The maximum relative error of the RSBO-SVR algorithm did not exceed 4% in both the simulation and experiment. It had a maximum reduction of 99.38% in runtime compared to SVR.