DYNAMIC SYSTEM IDENTIFICATION USING HIGH-ORDER HOPFIELD-BASED NEURAL NETWORK (HOHNN)

Abstract

The high-order Hopfield-based neural network (HOHNN) with functional link net (FLN) has been developed in this paper for the purpose of uncertain dynamic system identification. In comparison with the traditional Hopfield neural network (HNN) and the high-order neural network (HONN), the compact structure of FLN, with a systematic order mathematical representation combined into the proposed HOHNN, has additional inputs for each neuron for faster convergence rate and less computational load. The weighting factors in HOHNN are tuned via the Lyapunov stability theorem to guarantee the convergence performance of real-time system identification. The robust learning analysis of HOHNN to improve the convergence in the performance is also discussed. The simulation results and computation analysis for different Hopfield-based neural networks are conducted to show the effectiveness of HOHNN in uncertain dynamic system identification.