Improved delay-dependent stability result for neural networks with time-varying delays
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Updated: Feb 7, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
Published on: January 9, 2016
Hanyong Shao1, Huanhuan Li1, Lin Shao2
1The Research Institute of Automation, Qufu Normal University, Rizhao, 276826, China.
Abstract:
This paper is concerned with a new Lyapunov-Krasovskii functional (LKF) approach to the stability for neural networks with time-varying delays. The LKF has two features: First, it can make full use of the information of the activation function. Second, it employs the information of the maximal delayed state as well as the instant state and the delayed state. When estimating the derivative of the LKF we employ a new technique that has two characteristics: One is that Wirtinger-based integral inequality and an extended reciprocally convex inequality are jointly employed; the other is that the information of the activation function is used as much as we can. Based on Lyapunov stability theory, a new stability result is obtained. Finally, three examples are given to illustrate the stability result is less conservative than some recently reported ones.
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