A New Extension of Newton Algorithm for Nonlinear System Modelling Using RBF Neural Networks

Model performance and convergence rate are two key measures for assessing the methods used in nonlinear system identification using Radial Basis Function neural networks. A new extension of the Newton algorithm is proposed to further improve these two aspects by extending the results of recently proposed continuous forward algorithm (CFA) and hybrid forward algorithm (HFA). Computational complexity analysis confirms its efficiency, and numerical examples show that it converges faster and potentially outperforms CFA and HFA.