High-Order Hopfield-based Neural Network for Nonlinear System Identification

Abstract

The high-order Hopfield neural network (HOHNN) with functional link net has been developed in this paper for the purpose of system identification of nonlinear dynamical system. The weighting factors in HOHNN will be tuned via the Lyapunov stability criterion to guarantee the convergence performance of real-time system identification. In comparison with the traditional Hopfield neural network (HNN), the proposed architecture of HOHNN has additional inputs for each neuron which has the advantages of faster convergence rate and less computational load. The simulation results for both HNN and HOHNN are finally conducted to show the effectiveness of HOHNN in system identification of uncertain dynamical systems. It is obvious from the simulation results that the performance of system identification for HOHNN is better than that of HNN.