In order to improve the generalization capacity of neural network for poorly known nonlinear dynamic system
with long time-delay, a dynamic feedforward neural network on Gaussian particle swarm optimization algorithm is proposed.
The dynamic delay operators are added between the input and hidden layer, output and last hidden layer, which can effectively
identify the precise pure delay time. The nonlinear dynamic system model is built exactly to predict the system change. On
the other hand, Gaussian function and chaos mapping are adopted to balance the global optimization ability to overcome
premature convergence, which can optimize the parameters in the neural network structure adaptively. Compared with other
methods in the experiments, the effectiveness of the proposed method on nonlinear long-delay system identification is shown.