Improved stability criteria for generalized neural networks with time-varying delay by auxiliary function-based integral inequality. (English) Zbl 1419.35004
Summary: This paper is mainly concerned with improved stability criteria for generalized neural networks (GNNs) with time-varying delay by delay-partitioning approach. A newly augmented Lyapunov-Krasovskii functional (LKF) with triple integral terms is constructed by decomposing integral interval, in which the relationships between the augmented state vectors are fully taken into account. The tighter bounding inequalities such as a Wirtinger-based integral inequality, Peng-Park’s integral inequality, and an auxiliary function-based integral inequality are employed to effectively handle the cross-product terms occurred in derivative of the LKF. As a result, less conservative delay-dependent stability criterion can be achieved in terms of \(e_{s}\) and LMIs. Finally, two numerical examples are included to show that the proposed results are less conservative than existing ones.
MSC:
| 35A23 | Inequalities applied to PDEs involving derivatives, differential and integral operators, or integrals |
| 26D10 | Inequalities involving derivatives and differential and integral operators |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
generalized neural networks (GNNs); stability analysis; Lyapunov-Krasovskii functional (LKF); time-varying delay; delay-partitioning approach; auxiliary function-based integral inequalityReferences:
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