Application of modified Sigma-Pi-linked neural network to dynamical system identification

This paper describes the development of a self-feedback Sigma-Pi-linked (Σ-Π) backpropagation neural network and its applications to dynamical system identification. A self-feedback path is added to each neuron to generate the recursive effect. Each neuron output is recursively related by current input and its preceding output. The introduction of this self-feedback path enables the network to exhibit a dynamic characteristic. Using this complex Σ-Π-linked architecture, the developed network is capable of performing a system identification for a highly non-linear plant. In the last section of this paper, the function approximation property of this modified network is applied to system identification for different linear and non-linear dynamical systems. This paper also compares the modified network with the conventional backpropagation neural network. Simulation results show that the function approximation property of the modified network is encouraging and can be successfully applied to nonlinear dynamical system identification