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Updated: Apr 25, 2026

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
Published on: May 25, 2019
Improved stability criteria of static recurrent neural networks with a time-varying delay
Lei Ding1, Hong-Bing Zeng2, Wei Wang3
1School of Information Science and Engineering, Jishou University, Jishou 416000, China.
Abstract:
This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods.
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