Radial basis function networks with hybrid learning for system identification with outliers

Abstract

This paper demonstrates that radial basis function networks (RBFNs) with support vector regression (SVR) and annealing robust learning algorithm (ARLA) can be used effectively for the identification of the nonlinear dynamic systems with outliers. When the RBFNs are used for the identification of the nonlinear dynamic system, the number of hidden nodes, the initial parameters of the kernel, and the initial weights of the network must be determined first, a SVR approach is proposed to solve the initial problem of RBFNs. That is, the SVR uses the quadratic programming optimization to determine the initial structure of the RBFNs. Besides, the new cost function for the system identification with outliers is also proposed. That is, the proposed annealing robust radial basis function networks (ARRBFNs) are trained by the ARLA, which uses the annealing concept in the cost function of the robust back-propagation learning algorithm, can overcome the error measurement caused by the outliers. Simulation results show the superiority of the proposed method with different SVR.