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Stabilization of delayed Cohen‐Grossberg BAM neural networks
Author(s) -
Chinnathambi Rajivganthi,
Rihan Fathalla A.,
Shanmugam Lakshmanan
Publication year - 2017
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.4630
Subject(s) - control theory (sociology) , settling time , artificial neural network , controller (irrigation) , mathematics , lyapunov function , upper and lower bounds , algebraic number , full state feedback , function (biology) , control (management) , computer science , control engineering , nonlinear system , engineering , artificial intelligence , mathematical analysis , physics , step response , agronomy , quantum mechanics , biology , evolutionary biology
This paper deals with finite‐time stabilization results of delayed Cohen‐Grossberg BAM neural networks under suitable control schemes. We propose a state‐feedback controller together with an adaptive‐feedback controller to stabilize the system of delayed Cohen‐Grossberg BAM neural networks. Stabilization conditions are derived by using Lyapunov function and some algebraic conditions. We also estimate the upper bound of settling time functional for the stabilization, which depends on the controller schemes and system parameters. Two illustrative examples and numerical simulations are given to validate the success of the derived theoretical results.