A novel synchronization control with index adjuster for stochastic neural networks via trace similarity.

Based on the stability of the error system, we focus on the synchronization control of Stochastic Neural Networks (SNNs) with Time-Varying Delays (TVD). In order to observe the effect of the slave system following the drive system, we carry out the output result analysis of networks based on logic framework for the drive-follow systems. The constructive method and extremum theory are utilized to prove that the sample traces are similar to the state traces of the systems. For achieving the efficient control of the error system, the gain matrix of the controller with index regulator is designed to adjust the intensity of the parameters. Through one contrast analysis, the proposed control method is superior to the control approach given by comparative literature. Employing rigorous derivation, the exponential synchronous conditions are obtained based on trace similarity and the Lyapunov stability principle. In particular, one numerical example with three neurons fully validates the obtained results. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved.