Use of combined ARX - NARX model in identification of neuromuscular system

Abstract

Neural system that controls movement and posture is a highly nonlinear complex system. Its adaptability and easy accommodation to changes in environment and task specifications make it an ideal system. In this paper, the muscle control system from spinal cord to muscle displacement has been studied. At first, a detailed nonlinear model is simulated in Simulink based on an already developed work. Then, three system identification techniques are examined to estimate the behavior of this complex system. The first one is based on popular linear ARX model. Then, the system is modeled by NARX neural network (Nonlinear Autoregressive Network with Exogenous Inputs) which has a powerful structural network in modeling dynamical systems. Finally, a new method of modeling using combined NARX and ARX structure is proposed in which ARX gets the linear part of the system and the NARX picks up the nonlinearities. The simulation results demonstrate the superiority of the latter method with respect to other examined approaches.