Finite time anti-synchronization of complex-valued neural networks with bounded asynchronous time-varying delays

TL;DR

This paper establishes criteria for finite-time anti-synchronization in complex-valued neural networks with asynchronous delays, using a novel norm and decomposition technique, supported by numerical validation.

Contribution

It introduces new finite-time anti-synchronization criteria for CVNNs with asynchronous delays using a generalized norm and decomposition method.

Findings

Derived criteria for finite-time anti-synchronization

Demonstrated process divided into two finite phases

Validated results with a numerical example

Abstract

In this paper, we studied the finite time anti-synchronization of master-slave coupled complex-valued neural networks (CVNNs) with bounded asynchronous time-varying delays. With the decomposing technique and the generalized $\{\xi,\infty\}$-norm, several criteria for ensuring the finite-time anti-synchronization are obtained. The whole anti-synchronization process can be divided into two parts: first, the norm of each error state component will change from initial values to $1$ in finite time, then from $1$ to $0$ in fixed time. Therefore, the whole time is finite. Finally, one typical numerical example is presented to demonstrate the correctness of our obtained results.