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In this paper, the mean-square exponential stability problem for discrete-time recurrent neural networks with time-varying discrete and distributed delays is investigated. Considering the delay distributions, a novel class of Lyapunov functional is introduced. By exploiting all possible information in mixed time delays, a sufficient condition for the whole system to be mean-square exponentially stable is given. Numerical examples are proposed to illustrate the effectiveness of the method, and show that by using the approach in this paper, the obtained results are less conservative than the existing ones. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability analysis problem for uncertain stochastic discrete-time recurrent neural networks with time-varying delay. By using linear matrix inequality method and discrete Jensen inequality, a new Lyapunov–Krasovskii function is established to derive sufficient condition for globally asymptotical stability in mean square of the recurrent neural networks with stochastic disturbance. As an extension, we further consider the stability analysis problem for the same class of neural networks but with uncertainty. It is shown that the newly obtained result is less conservative than the existing ones when the described system is without disturbance and uncertainty. Meanwhile, the computational complexity is reduced since less variable is involved. Two numerical examples are presented to illustrate the effectiveness and the benefits of the proposed method. [ABSTRACT FROM AUTHOR]