Bipartite Synchronization of Cooperation–Competition Neural Networks Using Asynchronous Sampling Scheme

This paper investigates the bipartite synchronization problem for cooperation–competition neural networks (CCNNs) under asynchronous sampling control. First, using signed graph theory to characterize the interrelationships between cooperation and competition, a mathematical model for cooperative–competitive neural networks is established. To formulate the error systems of such networks, a Laplacian matrix with zero row sum is derived through coordinate transformation techniques. Considering network complexity and deception attack impacts, an asynchronous sampling-based secure control scheme is designed while preserving performance guarantees. By relaxing positive definiteness constraints, a class of looped functionals is introduced. State norm estimations are utilized to derive criteria for achieving bipartite synchronization. The feedback gain matrices of the asynchronous sampling controller are obtained by solving linear matrix inequalities. Finally, numerical simulations validate the effectiveness of the proposed method.