TY - JOUR
T1 - Fixed-time stability of Cohen-Grossberg BAM neural networks with impulsive perturbations
AU - Jamal, Md Arzoo
AU - Kumar, Rakesh
AU - Mukhopadhyay, Santwana
AU - Kwon, Oh Min
N1 - Funding Information:
The authors are extending their heartfelt thanks to the revered reviewers for their constructive suggestions towards the improvement of the article. This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2020R1A6A1A12047945 and in part by the Grand Information Technology Research Center Support Program supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP) under Grant IITP-2023-2020-0-01462.
Funding Information:
Rakesh Kumar earned his B.Sc. and M.Sc. degrees in Mathematics from Magadh University, Bodh Gaya, Bihar, India in 2012 and 2014 respectively. He completed his Ph.D. from the Department of Mathematical Sciences, IIT (BHU) Varanasi, India in 2021. Presently, he is serving as an Alexander von Humboldt Research Fellow at Karlsruhe Institute of Technology (KIT), Germany. During his doctoral studies, he worked as a Raman-Charpak Fellow at INRIA, Lille, France in 2020. Later in 2022, he was employed as a postdoctoral fellow at Ben-Gurion University of the Negev, Israel. He also qualified the Graduate Aptitude Test in Engineering (GATE) in 2015. Mr. Kumar was a recipient of the Junior Research Fellowship (JRF) Certificate in 2015 from UGC and from the Council of Scientific and Industrial Research (CSIR) in 2016. After completing his Ph.D., he was awarded the National Post Doctoral fellowship (NPDF) from SERB-Gov. Of India in 2022 and a Research fellowship from the Alexander von Humboldt Foundation, Germany in 2022.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/9/14
Y1 - 2023/9/14
N2 - This article concerns the problem of fixed-time stability (FXTS) of a Cohen-Grossberg bidirectional associative memory neural network (CGBAMNN) with destabilizing impulsive effects. A novel sufficient condition for the impulsive dynamical systems (IDSs) to be FXTS for destabilizing impulses is obtained. Different from the usual Lyapunov inequality for FXTS of IDSs, we have applied a new Lyapunov inequality to obtain the results under impulsive perturbations. Based on the average impulsive interval (AII) and the comparison principle, we have derived the results of this paper. Two types of continuous controllers: one with signum terms and another without signum terms, based on a new Lyapunov inequality, FXTS of CGBAMNN have been studied. The settling-time functions obtained in this article depend on the parameters of the impulsive sequences. Finally, two numerical examples, one is a cyber-physical system with deception attacks and another is a neural network, are given to validate the efficiency of our obtained theoretical results.
AB - This article concerns the problem of fixed-time stability (FXTS) of a Cohen-Grossberg bidirectional associative memory neural network (CGBAMNN) with destabilizing impulsive effects. A novel sufficient condition for the impulsive dynamical systems (IDSs) to be FXTS for destabilizing impulses is obtained. Different from the usual Lyapunov inequality for FXTS of IDSs, we have applied a new Lyapunov inequality to obtain the results under impulsive perturbations. Based on the average impulsive interval (AII) and the comparison principle, we have derived the results of this paper. Two types of continuous controllers: one with signum terms and another without signum terms, based on a new Lyapunov inequality, FXTS of CGBAMNN have been studied. The settling-time functions obtained in this article depend on the parameters of the impulsive sequences. Finally, two numerical examples, one is a cyber-physical system with deception attacks and another is a neural network, are given to validate the efficiency of our obtained theoretical results.
KW - Average impulsive interval (AII)
KW - BAM neural networks
KW - Cohen-Grossberg neural networks
KW - Fixed-time stability
KW - Impulsive dynamical systems (IDSs)
UR - http://www.scopus.com/inward/record.url?scp=85164034566&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2023.126501
DO - 10.1016/j.neucom.2023.126501
M3 - Article
AN - SCOPUS:85164034566
SN - 0925-2312
VL - 550
JO - Neurocomputing
JF - Neurocomputing
M1 - 126501
ER -