Global dissipativity of Clifford-valued multidirectional associative memory neural networks with mixed delays

Abstract

The main goal of this article is to study the global dissipativity problem of Clifford-Valued Multidirectional Associative Memory Neural Networks (CVMAMNNs) with time-varying delays and distributed delays. Based on Lyapunov functionals and Linear Matrix Inequalities (LMIs) approach, new sufficient conditions are derived to ensure the global dissipativity and global exponential dissipativity of the considered network model. Moreover, the global attractive set and global exponential attractive set are obtained which are positive invariant ones. Finally, two numerical examples with simulations are given to illustrate the effectiveness of the analytical findings.