An Adaptive dynamic evolution feedforward neural network on modified particle swarm optimization

Abstract

In order to improve the generalization capacity of neural networks for poorly known nonlinear dynamic system with long time-delay, a novel adaptive dynamic feedforward neural network on modified Particle Swarm Optimization (PSO) algorithm is proposed. The adaptive time delay operator is adopted between input layer and the first hidden layer, and also the last hidden layer and output layer. Utilizing these dynamic time delay parameters, the proposed structure can adequately identify different classes of nonlinear systems expressed in the input-output representation form and pure time delay. Otherwise, to overcome the particles' premature convergence, the white noise and Logistic mapping are used to enhance the particles' search performance. Furthermore, the parameters in the dynamic feedforward neural network are trained by the modified PSO method. The proposed neural network shows a satisfactory global search and quick convergence capability, avoiding the complexity of gradient calculation. Simulation results demonstrate that the proposed algorithm is effective and accurate in identifying long-time delay nonlinear systems through the comparison with other methods.