Stability Analysis on Cohen–Grossberg Neural Networks with Saturated Impulse Inputs

  • Renyi Xie
  • Chuandong LiEmail author


This paper aims to analyze the stability of Cohen–Grossberg neural networks with saturated impulse inputs. By using the method of Lyapunov functions, convex analysis and matrix inequality, some sufficient conditions are obtained to ensure the stability of the Cohen–Grossberg networks with saturated impulse inputs, including full state constraints and partial state constraints. And some conservative corollaries are obtained. In addition, the fixed time-delay network with fixed impulsive inputs is analyzed by the similar method. Meanwhile, the effectiveness and validity are verified by some numerical examples.


Cohen–Grossberg neural networks Stability analysis Lyapunov functions Saturated impulsive input Fixed time delay 



The authors express sincerest gratitude to the editors and anonymous reviewers for their constructive comments, which have played an significant role in improving this paper. This work was supported by the National Natural Science Foundation of China under Grants 61873213 and 61633011, and in part by the Chongqing Research Program of Basic Research and Frontier Technology of cstc2015jcyjBX0052.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing), College of Electronic and Information EngineeringSouthwest UniversityChongqingChina
  2. 2.College of Electronic and Information EngineeringSouthwest UniversityChongqingChina

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