数学
指数稳定性
有界函数
理论(学习稳定性)
人工神经网络
规范(哲学)
控制理论(社会学)
应用数学
计算机科学
非线性系统
数学分析
人工智能
机器学习
物理
控制(管理)
量子力学
政治学
法学
作者
R. Suresh,A. Manivannan
摘要
We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalities, the delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs). We discuss the parameter uncertainties in terms of norm-bounded conditions in the given interval with constant delay. The derived conditions ensure that the global, asymptotic stability of the states for the proposed SNNs. We verify the effectiveness and applicability of the proposed criteria with numerical examples.
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