Premium
Enhanced result on stability analysis of randomly occurring uncertain parameters, leakage, and impulsive BAM neural networks with time‐varying delays: Discrete‐time case
Author(s) -
Sowmiya C.,
Raja R.,
Cao Jinde,
Rajchakit G.
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2883
Subject(s) - artificial neural network , bernoulli's principle , control theory (sociology) , discrete time and continuous time , a priori and a posteriori , computer science , stability (learning theory) , convex combination , mathematics , matlab , regular polygon , convex optimization , artificial intelligence , machine learning , control (management) , engineering , statistics , philosophy , geometry , epistemology , aerospace engineering , operating system
Summary In real‐world problems, neural networks play an increasingly important role in terms of both theory and applications. In this paper, the asymptotic stability analysis issue is investigated for uncertain impulsive discrete‐time bidirectional associative memory neural networks with leakage and time‐varying delays. With the assistance of novel summation inequality, reciprocally convex combination technique, and triple Lyapunov‐Krasovskii functionals terms, many cases of time‐varying delays are examined to certify the stability of neural networks. Here, the uncertainties are considered as a randomly occurring parameter uncertainty, and it conforms certain mutually uncorrelated Bernoulli‐distributed white noise sequences. An important feature of the results reported here is that the probability of occurrence of the parameter uncertainties specify a priori estimate. Finally, numerical examples are proposed to expose the capability and efficiency of our research work with the help of the LMI control toolbox in MATLAB.