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-∏) Back-propagation 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 - ∏-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 Back-propagation 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.