Fixed-Time Multi-Almost-Periodicity in Switched Fuzzy Neural Networks With Multicontroller Strategies

Abstract

This article provides theoretical analysis of the fixed-time multi-almost-periodicity in switched fuzzy neural networks, employing multicontroller strategies and a state-dependent switching mechanism. Utilizing the Ascoli–Arzela theorem, the properties of $M$-matrix, Lyapunov functions method, and some inequality techniques, we establish some sufficient conditions to ascertain that the number of exponentially stable almost-periodic solutions can be up to $4^{n}$, where $n$ is the number of neurons. Furthermore, we design various controllers to achieve the fixed-time stability for various almost-periodic solutions located in the positive invariant sets. Then, the settling time for the switched fuzzy networks to achieve multi-almost-periodicity is estimated. It is noteworthy to state that this article considers fixed-time multiperiodicity and fixed-time multistability as special cases of fixed-time multi-almost-periodicity. Two numerical examples are presented to demonstrate the theoretical results.