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Effects of leakage delay on global asymptotic stability of complex‐valued neural networks with interval time‐varying delays via new complex‐valued Jensen's inequality
Author(s) -
Samidurai R.,
Sriraman R.,
Cao Jinde,
Tu Zhengwen
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.2914
Subject(s) - matlab , exponential stability , artificial neural network , linear matrix inequality , interval (graph theory) , control theory (sociology) , mathematics , stability (learning theory) , toolbox , leakage (economics) , computer science , mathematical optimization , nonlinear system , artificial intelligence , control (management) , physics , quantum mechanics , combinatorics , machine learning , economics , macroeconomics , programming language , operating system
Summary This paper investigates the global asymptotic stability analysis for a class of complex‐valued neural networks with leakage delay and interval time‐varying delays. Different from previous literature, some sufficient information on a complex‐valued neuron activation function and interval time‐varying delays has been considered into the record. A suitable Lyapunov‐Krasovskii functional with some delay‐dependent terms is constructed. By applying modern integral inequalities, several sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed system model. All the proposed criteria are formulated in the structure of a complex‐valued linear matrix inequalities technique, which can be checked effortlessly by applying the YALMIP toolbox in MATLAB linear matrix inequality. Finally, two numerical examples with simulation results have been provided to demonstrate the efficiency of the proposed method.