Stability analysis of delayed fuzzy Cohen‐Grossberg neural networks with discontinuous activations

Abstract

SummaryIn this article, we investigate the dynamical behavior of a class of delayed fuzzy Cohen‐Grossberg neural networks (FCGNNs) with discontinuous activation functions subject to time delays and fuzzy terms. By using the inequality analysis technique and the M‐matrix theory, sufficient and proper conditions are given in order to establish the existence, convergence, and global exponential stability of equilibrium point of the system. In particular, we discuss the impact of discontinuous neuron activations on the existence and exponential stability of equilibrium point for FCGNNs. Two numerical examples are provided to substantiate the theoretical results.

Finite-time complete periodic synchronization of memristive neural networks with mixed delays

“…They can play the role of memory through feedback and are perfectly able to receive sensory data from our future agent 3 , 4 . In particular, continuous time RNNs (CTRNNs) are RNNs modeled by dynamical systems in the form of differential equation; they combine machine learning and physical modeling 5 – 7 . In fact, CTRNNs are mathematically easier to analyze, and continuous formulation offers more flexibility in adapting the system to the problem and adding constraints.…”