Global robust exponential synchronization of neutral-type interval Cohen–Grossberg neural networks with mixed time delays

Abstract

Global robust exponential synchronization of neutral-type interval Cohen–Grossberg neural networks (NTICGNNs) with mixed time delays is analyzed in this paper. The system model, in which unrepresentable using vector-matrices, renders certain methods unsuitable for addressing issues within the vector-matrix framework. To address this limitation, a direct method based on system solutions is proposed, and sufficient conditions are provided to achieve global robust exponential synchronization of the considered NTICGNNs. This approach results in a global robust exponential synchronization criterion without the need to establish any Lyapunov-Krasovskii functionals. The applicability of the obtained synchronization conditions is validated using two numerical examples. Additionally, the obtained theoretical results have been applied to solve a quadratic programming problem and to encrypt and decrypt color images, demonstrating the practical application value of our research results. Notably, this study presents the first global robust exponential synchronization analysis for the considered NTICGNNs and proposes a novel approach grounded in system solutions.