Open AccessSynchronizations of fuzzy cellular neural networks with proportional time-delay
Author(s) -
Ankit Kumar,
Subir Das,
Vijay K. Yadav,
. Rajeev,
Jinde Cao,
Chuangxia Huang
Publication year - 2021
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
ISSN - 2473-6988
DOI - 10.3934/math.2021617
Subject(s) - settling time , synchronization (alternating current) , fuzzy logic , artificial neural network , mathematics , computer science , stability (learning theory) , control theory (sociology) , topology (electrical circuits) , combinatorics , artificial intelligence , control (management) , machine learning , control engineering , engineering , step response
In this article, finite-time and fixed-time synchronizations (FFTS) of fuzzy cellular neural networks (FCNNs) with interaction and proportional delay terms have been investigated. The synchronizations of FCNNs are achieved with the help of p -norm based on the inequalities defined in Lemmas 2.1 and 2.2. The analysis of the method with some useful criteria is also used during the study of FFTS. Under the Lyapunov stability theory, FFTS of fuzzy-based CNNs with interaction and proportional delay terms can be achieved using controllers. Moreover, the upper bound of the settling time of FFTS is obtained. In view of settling points, the theoretical results on the considered neural network models of this article are more general as compared to the fixed time synchronization (FTS). The effectiveness and reliability of the theoretical results are shown through two numerical examples for different particular cases.