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New Results on Exponential Stability of Competitive Neural Networks with Multi‐Proportional Delays
Author(s) -
Qin Jiali,
Li Yongkun
Publication year - 2020
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1926
Subject(s) - exponential stability , verifiable secret sharing , class (philosophy) , artificial neural network , control theory (sociology) , mathematics , stability (learning theory) , lyapunov function , exponential function , exponential growth , fixed point theorem , computer science , mathematical analysis , artificial intelligence , nonlinear system , physics , control (management) , machine learning , quantum mechanics , set (abstract data type) , programming language
In this paper, we are concerned with a class of competitive neural networks with multi‐proportional delays. By applying the Banach fixed point theorem and constructing suitable Lyapunov functions, we obtain new sufficient conditions for the global exponential stability to this class of neural networks, which are easily verifiable. Finally, two examples are given to illustrate the effectiveness of the obtained results.