Premium
Stability and stabilization for stochastic Cohen‐Grossberg neural networks with impulse control and noise‐induced control
Author(s) -
Guo Yingxin,
Cao Jinde
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4379
Subject(s) - control theory (sociology) , impulse (physics) , artificial neural network , impulse control , exponential stability , controller (irrigation) , noise (video) , mathematics , stability (learning theory) , computer science , impulse noise , control (management) , artificial intelligence , nonlinear system , machine learning , physics , psychology , pixel , quantum mechanics , agronomy , image (mathematics) , psychotherapist , biology
Summary By applying the It ô formula, the Gronwall inequality, and the law of large numbers technique, a new simple sufficient inequality condition is presented for the almost surely exponential stability of the stochastic Cohen‐Grossberg neural networks with impulse control and time‐varying delays. Moreover, a new result is also given for the existence of unique states of the systems. An impulsive controller and a suitable noise controller are also given at the same time. The condition contains and improves some of the previous results in the earlier references.