Structured parameter optimization method for the radial basis function-based state-dependent autoregressive model

An off-line structured nonlinear parameter optimization method (SNPOM) for accelerating the computational convergence of parameter estimation of the radial basis function-based state-dependent autoregressive (RBF-AR) model is proposed. Using the method, all the parameters of the RBF-AR model may be optimized automatically and simultaneously. The proposed method combines the advantages of the Levenberg-Marquardt algorithm in nonlinear parameter optimization and the least-squares method in linear parameter estimation. Case studies on two complex time series and a nonlinear chemical reaction process show that the proposed parameter optimization method exhibits significantly accelerated convergence when compared with the classic version of the Levenberg-Marquardt algorithm, and to some hybrid algorithms such as the evolutionary programming algorithm.