Linear Programming SVM-ARMA 2K With Application in Engine System Identification

As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Nevertheless, the conventional quadratic programming support vector regression can easily lead to representation redundancy and expensive computational cost. In this paper, by using the norm minimization and taking account of the different characteristics of autoregression (AR) and the moving average (MA), an innovative nonlinear dynamical system identification approach, linear programming SVM-ARMA , is developed to enhance flexibility and secure model sparsity in identifying nonlinear dynamical systems. To demonstrate the potential and practicality of the proposed approach, the proposed strategy is applied to identify a representative dynamical engine model. Note to Practitioners—The identification of complex nonlinear engine systems poses challenges on the efficiency, flexibility and generalization capability of the identification algorithms. Inspired by the triumphs of support vector learning methodology in pattern recognition and regression analysis, an innovational nonlinear systems identification algorithm, LP-SVM-ARMA was developed in this paper as a significant stride toward the fusion of modern machine learning essentials into practical identification strategy for industrial systems. In particular, the possible generalization of LP-SVM-ARMA via more complex composite kernel functions is also discussed to meet the diversity of industrial practice.