Integration of support vector regression and annealing dynamical learning algorithm for MIMO system identification

This paper presents a robust approach to identify multi-input multi-output (MIMO) systems. Integrating support vector regression (SVR) and annealing dynamical learning algorithm (ADLA), the proposed method is adopted to optimize a radial basis function network (RBFN) for identification of MIMO systems. In the system identification, first, SVR is adopted to determine the number of hidden layer nodes, the initial structure of the RBFN. After initialization, ADLA with nonlinear time-varying learning rate is then applied to train the RBFN. In the ADLA, the determination of the learning rate would be an important work for the trade-off between stability and speed of convergence. A computationally efficient optimization method, particle swarm optimization (PSO) method, is adopted to simultaneously find optimal learning rates. Due to the advantages of SVR and ADLA (SVR-ADLA), the proposed RBFN (SVR-ADLA-RBFN) has good performance for MIMO system identification. Two examples are illustrated to show the feasibility and superiority of the proposed SVR-ADLA-RBFNs for identification of MIMO systems. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

► Integrate support vector regression (SVR) and annealing dynamical learning algorithm (ADLA) to optimize a radial basis function network (RBFN) for identification of MIMO systems. ► SVR is adopted to determine the number of hidden layer nodes, the initial structure of the RBFN. ► ADLA with nonlinear time-varying learning rate is then applied to train the RBFN. ► PSO method is adopted to simultaneously find optimal learning rates.