Black-box identification of a pilot-scale dryer model: A Support Vector Regression and an Imperialist Competitive Algorithm approach

Abstract

This report describes system identification by means of the hybrid black-box method. The identification was carried out on the regulation object (a dryer model) representing the pilot-scale processes occurring during air conditioning and drying. The new approach proposed in the paper is the use of the imperialist competitive algorithm (ICA) as a tool for selecting the best parameters for support vector regression (SVR) and for selecting an optimal set of regressors. The advantage of this method is that the selection of an optimal set of regressors and the optimal parameters of SVR for this set is performed automatically, which reduces the time needed for identification. The results of the SVR with the ARX, ARMAX, OE, Box-Jenkins (BJ) and low-order transfer function (Tf) models were compared. The research was conducted for two fan speeds, equal to 40% and 60%. The Fit and MSE indicators for the SVR achieved a higher value with respect to those of the ARX, ARMAX, OE, BJ and Tf models. This method is sufficiently universal and can be applied to any plant as an efficient alternative method. This report is supported by National Science Centre grant UMO-2015/17/B/NZ7/02937