The neural network state observer design based on the particle swarm optimization-black stork foraging process

Abstract

As RBF (Radial Basis Function) neural networks can approximate any nonlinear function in a compact set with arbitrary precision, this paper presents an approach of the state observer design for a class of nonlinear systems by using the RBF neural network. In order to enhance the learning ability of the RBF neural network, a hybrid black stork foraging process algorithm based on PSO (Particle Swarm Optimization) is proposed. Furthermore, a Lyapunov function is used for analyzing the stability of the RBF state observer. The simulation results demonstrate that the proposed RBF neural network state observer can estimate the state quickly and accurately.