Mean-square exponential input-to-state stability of stochastic fuzzy recurrent neural networks with multiproportional delays and distributed delays
Summary: We are interested in a class of stochastic fuzzy recurrent neural networks with multiproportional delays and distributed delays. By constructing suitable Lyapunov-Krasovskii functionals and applying stochastic analysis theory, It \(\hat{o} \)'s formula and Dynkin's formula, we derive novel sufficient conditions for mean-square exponential input-to-state stability of the suggested system. Some remarks and discussions are given to show that our results extend and improve some previous results in the literature. Finally, two examples and their simulations are provided to illustrate the effectiveness of the theoretical results.
scientific article; zbMATH DE number 7019271
scientific article