Improved Results on Finite-Time Passivity and Synchronization Problem for Fractional-Order Memristor-Based Competitive Neural Networks: Interval Matrix Approach

Improved Results on Finite-Time Passivity and Synchronization Problem for Fractional-Order Memristor-Based Competitive Neural Networks: Interval Matrix Approach

This research paper deals with the passivity and synchronization problem of fractional-order memristor-based competitive neural networks (FOMBCNNs) for the first time. Since the FOMBCNNs’ parameters are state-dependent, FOMBCNNs may exhibit unexpected parameter mismatch when different initial condit...

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Bibliographic Details
Main Authors: Pratap Anbalagan, Raja Ramachandran, Jehad Alzabut, Evren Hincal, Michal Niezabitowski
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Fractal and Fractional
Subjects:
memristor
fractional-order competitive neural networks
finite-time passivity
finite-time boundedness
finite-time synchronization
Online Access:https://www.mdpi.com/2504-3110/6/1/36

Internet

https://www.mdpi.com/2504-3110/6/1/36

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