Existence and Exponential Stability of Equilibrium Point for Fuzzy BAM Neural Networks with Infinitely Distributed Delays and Impulses on Time Scales

Abstract

By using the fixed point theorem and constructing a Lyapunov functional, we establish some sufficient conditions on the existence, uniqueness, and exponential stability of equilibrium point for a class of fuzzy BAM neural networks with infinitely distributed delays and impulses on time scales. We also present a numerical example to show the feasibility of obtained results. Our example also shows that the described time and continuous neural time networks have the same dynamic behaviours for the stability.

Exponential and fixed‐time stabilization of memristive neural networks with mixed delays

“…Cao et al 10 discussed the stabilization of MNNs by designing appropriate event-based controllers, while Wang et al 11 proved that the stabilization of MNNs by using the fuzzy membership functions-dependent on Lyapunov-Krasovskii functional. The existence and exponential stabilization of the system at the equilibrium point were discussed in other studies, [12][13][14][15][16] and the exponential synchronization and stabilization of NNs were investigated in other studies [17][18][19][20][21] by adopting the sampled-data control.…”