Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays

• We discuss exponential p-convergence for stochastic BAM neural networks.
• The delays here are multiple time-varying and infinite distributed delays.
• We establish a new LL-operator delay differential-integral inequality.
• Delay-dependent criteria ensuring exponential p-convergence are obtained.
• Giving the detail estimations of the exponential p-convergence ball.

This paper is concerned with the problem of global exponential p  -convergence for stochastic BAM neural networks with time-varying and infinite distributed delays. By constructing a new delay differential-integral inequality and a novel LL-operator differential-integral inequality, and coupling with stochastic analysis techniques, some delay-dependent sufficient conditions are derived to guarantee exponential p-convergence and the state variables of the discussed stochastic BAM neural networks are globally exponentially convergent to a ball in the state space with a pre-specified convergence rate. Meanwhile, the exponential p-convergent balls are also estimated. Here, the existence and the uniqueness of the equilibrium point needs not to be considered. Finally, two examples with numerical simulations are given to illustrate the effectiveness of the theoretical results.