Premium
Stability analysis and robust synchronization of fractional‐order competitive neural networks with different time scales and impulsive perturbations
Author(s) -
Pratap Anbalagan,
Raja Ramachandran,
Agarwal Ravi P,
Cao Jinde
Publication year - 2019
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.3056
Subject(s) - synchronization (alternating current) , mathematics , control theory (sociology) , representation (politics) , stability (learning theory) , class (philosophy) , linear matrix inequality , artificial neural network , computer science , mathematical optimization , topology (electrical circuits) , control (management) , artificial intelligence , combinatorics , machine learning , politics , political science , law
Summary This article mainly examine a class of robust synchronization, global stability criterion, and boundedness analysis for delayed fractional‐order competitive type‐neural networks with impulsive effects and different time scales. Firstly, by endowing the robust analysis skills and a new class of Lyapunov‐Krasovskii functional approach, the error dynamical system is furnished to be a robust adaptive synchronization in the voice of linear matrix inequality (LMI) technique. Secondly, by ignoring the uncertain parameter terms, the existence of equilibrium points are established by means of topological degree properties, and the solution representation of the considered network model are provided. Thirdly, a novel global asymptotic stability condition is proposed in the voice of LMIs, which is less conservative. Finally, our analytical results are justified with two numerical examples with simulations.