Identification of nonlinear dynamic systems using convex combinations of multiple adaptive radius basis function networks

To improve performance of nonlinear adaptive filter based on radius basis function (RBF) networks, a generalized combination scheme is proposed for nonlinear dynamic system identification in this paper. The nonlinear filter proposed is constructed by the convex combination of multiple RBF networks (MCRBF). Its adaptive algorithm with different step sizes is derived by the gradient descent rule, and can overcome the contradiction between convergence speed and precision of the stochastic gradient (SG) algorithm for RBF networks, which is imposed by the selection of a fixed value for the adaption step. Computer simulations demonstrate that the performance of the nonlinear filter proposed is superior to the RBF for nonlinear dynamic system identification in terms of convergence speed, steady state error and tracking capability.

► To alleviate the compromise between speed and precision, a MCRBF is proposed.
► Adaptive algorithm with different step sizes for MCRBF is derived by the SG rule.
► Nonlinear dynamic system identification based on the MCRBF has been carried out.