Neural Network based Hammerstein System Identification using Particle Swarm Subspace Algorithm

This paper presents a new method for modeling of Hammerstein systems. The developed identification method uses state-space model in cascade with radial basis function (RBF) neural network. A recursive algorithm is developed for estimating neural network synaptic weights and parameters of the state-space model. No assumption on the structure of nonlinearity is made. The proposed algorithm works under the weak assumption of richness of inputs. The problem of modeling is solved as an optimization problem and Particle Swarm Optimization (PSO) is used for neural network training. Performance of the algorithm is evaluated in the presence of noisy data and Monte-Carlo simulations are performed to ensure reliability and repeatability of the identification technique.