Modified Elman Spike Neural Network for Identification and Control of Dynamic System

Nadia Adnan Shiltagh Al-Jamali,Hamed S. Al-Raweshidy

doi:10.1109/access.2020.2984311

Abstract

The utilization of conventional modeling strategies in the identification and control of a nonlinear dynamical system suffers from some weaknesses. These include absence of precise, conventional knowledge about the system, a high degree of uncertainty, strongly nonlinear and time-varying behavior. In this paper, a modified training algorithm for the identification and control of a nonlinear system using a soft-computing approach is proposed. Specifically, a modified structure of the Elman neural network with spike neural networks is proposed. This modified structure includes self-feedback, which provides a dynamic trace of the training algorithm. This self-feedback has weights, which can be trained during the training process. The simulation results show that the modified structure with the modified training algorithm is capable of the identification and control of a dynamic system in a more robust manor than when solely applying the other types of neural networks by 70% in terms of minimization of the percentage of error.

Highlights

Neural networks (NNs) for identification and control have been receiving increasing attention, because they can establish optimal identification and control signals due to the online training process [1]
A survey reported in [1] led to the proposal of an intelligent adaptive dynamic control system based on a recurrent wavelet Elman NN for an induction motor servo drive
The results showed that the proposed structure increases the uncertainty identifier and speeds up the convergence time, which means that both the convergence precision and convergence time are enhanced better than with the basic Elman NN

Summary

INTRODUCTION

Neural networks (NNs) for identification and control have been receiving increasing attention, because they can establish optimal identification and control signals due to the online training process [1]. VOLUME 8, 2020 have the potential for quick adaptation [17]–[23] Given these advantages, as above mentioned, an SNN with an Elman NN is considered in this paper for the identification and control of dynamic plants. The time of postsynaptic potential begins to rise, as seen in Fig.2b,and there is a synapse sequence in the connection.The weight of each synapse effect on the spike-response function ζ represents the activation function of the neuron. THE PROPOSED CONTROLLER Fig. shows MESNN as a controller for the plant, the weights of which are updated during the modified training algorithm, as described latter. The output signal of MESNN is spike characterized with a spike time, which is converted into a real value applied as input to the plant

MODIFIED TRAINING ALGORITHM

Findings

VIII. CONCLUSION