Open AccessDelay-coupled fractional order complex Cohen-Grossberg neural networks under parameter uncertainty: Synchronization stability criteria
Author(s) -
Pratap Anbalagan,
Evren Hınçal,
R. Raja,
Dumitru Băleanu,
Jinde Cao,
Chuangxia Huang,
Michał Niezabitowski
Publication year - 2021
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2021172
Subject(s) - kronecker product , synchronization (alternating current) , linear matrix inequality , stability (learning theory) , artificial neural network , mathematics , control theory (sociology) , lyapunov stability , stability conditions , kronecker delta , computer science , topology (electrical circuits) , mathematical optimization , artificial intelligence , combinatorics , machine learning , physics , statistics , control (management) , discrete time and continuous time , quantum mechanics
This paper inspects the issues of synchronization stability and robust synchronization stability for fractional order coupled complex interconnected Cohen-Grossberg neural networks under linear coupling delays. For investigation of synchronization stability results, the comparison theorem for multiple delayed fractional order linear system is derived at first. Then, by means of given fractional comparison principle, some inequality methods, Kronecker product technique and classical Lyapunov-functional, several asymptotical synchronization stability criteria are addressed in the voice of linear matrix inequality (LMI) for the proposed model. Moreover, when parameter uncertainty exists, we also the investigate on the robust synchronization stability criteria for complex structure on linear coupling delayed Cohen-Grossberg type neural networks. At last, the validity of the proposed analytical results are performed by two computer simulations.