Open AccessExponential stabilisation of stochastic memristive neural networks under intermittent adaptive control
Author(s) -
Li Xiaofan,
Fang Jianan,
Li Huiyuan
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2017.0021
Subject(s) - stochastic neural network , control theory (sociology) , differential inclusion , artificial neural network , adaptive control , exponential stability , computer science , exponential function , lyapunov function , stochastic differential equation , brownian motion , stochastic process , controller (irrigation) , mathematics , control (management) , mathematical optimization , recurrent neural network , nonlinear system , artificial intelligence , mathematical analysis , statistics , physics , quantum mechanics , agronomy , biology
This study focuses on the exponential stabilisation problem for a general class of memristive neural networks subjected to both stochastic disturbance and time‐varying delays under periodically intermittent adaptive control. The stochastic disturbances are described as Brownian motions in the considered networks. An adaptive updated rule and a periodically intermittent adaptive control strategy are designed for the exponential stabilisation of memristive neural networks subjected to both stochastic disturbance and time‐varying delays. Then, by adopting the adaptive control technique, differential inclusion theory and set‐valued maps, and by building a new Lyapunov–Krasovskii functional, many novel sufficient conditions are proposed to guarantee exponential stabilisation for stochastic memristive neural networks. Different from existing results on stabilisation of stochastic memristive neural networks, the obtained criteria in this study are directly derived according to the parameters of networks. Finally, an example is carried out to demonstrate the validity of the theoretic results.