A parameter optimization method for radial basis function type models

作者：Peng H; Ozaki T; Haggan Ozaki V; Toyoda Y

来源：IEEE Transactions on Neural Networks, 2003, 14(2): 432-438.

DOI：10.1109/TNN.2003.809395

• identification
• nonlinear systems
• parameter estimation
• radial basis RBF)
• AutoRegressive model with eXogenous variable (RBF-ARX)
• RBF neural network
• state-dependent model

This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis RBF) networks and an RBF network-style coefficients AutoRegressive model with eXogenous variable (RBF-ARX) model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the Levenberg-Marquardt method (LMM) for nonlinear parameter optimization and partly on the least-squares method (LSM) using singular value decomposition (SVD) for linear parameter estimation. When. compared with some other algorithms, the SNPOM accelerates the computational convergence of the parameter optimization search process of RBF-type models. The usefulness of this approach is illustrated by means of several examples.

• 出版日期2003-3
• 单位中南大学