Identification of Transducer Nonlinear Dynamic System Using Hammerstein Neural Network

For identification nonlinear dynamic model of transducer,a novel Hammerstein neural network structure is presented.Firstly,the nonlinear dynamic system is described by a Hammerstein model which consists of a nonlinear static gain in cascade with a linear dynamic part.Secondly,a novel neural network structure is designed,in which the weights are corresponding with the parameters of the Hammerstein model,backward-propagation methods for the adjustment of weights in the network are discussed.Finally,parameters of the nonlinear static gain part and the linear dynamic in the Hammerstein model are determined simultaneously by iterative training.A numerical simulation of Hammerstein model is provided to validate the effectiveness.Simulation results show that the suggested identification schemes are practically feasible.