Multisynchronization of Interconnected Memristor-Based Impulsive Neural Networks with Fuzzy Hybrid Control

Hu, B, Guan, Z, Yu, X and Luo, Q 2018, 'Multisynchronization of Interconnected Memristor-Based Impulsive Neural Networks with Fuzzy Hybrid Control', IEEE Transactions on Fuzzy Systems, vol. 26, no. 5, pp. 3069-3084.


Document type: Journal Article
Collection: Journal Articles

Title Multisynchronization of Interconnected Memristor-Based Impulsive Neural Networks with Fuzzy Hybrid Control
Author(s) Hu, B
Guan, Z
Yu, X
Luo, Q
Year 2018
Journal name IEEE Transactions on Fuzzy Systems
Volume number 26
Issue number 5
Start page 3069
End page 3084
Total pages 16
Publisher IEEE
Abstract This paper studies a class of heterogeneous delayed impulsive neural networks with memristors and their collective evolution for multisynchronization. The multisynchronization represents a diversified collective behavior that is inspired by multitasking as well as observations of heterogeneity and hybridity arising from system models. In view of memristor, the memristor-based impulsive neural network is first represented by an impulsive differential inclusion. According to the memristive and impulsive mechanism, a fuzzy logic rule is introduced, and then, a new fuzzy hybrid impulsive and switching control method is presented correspondingly. It is shown that using the proposed fuzzy hybrid control scheme, multisynchronization of interconnected memristor-based impulsive neural networks can be guaranteed with a positive exponential convergence rate. The heterogeneity and hybridity in system models, thus, can be indicated by the obtained error thresholds that contribute to the multisynchronization. Numerical examples are presented and compared to demonstrate the effectiveness of the developed theoretical results.
Subject Control Systems, Robotics and Automation
Neural, Evolutionary and Fuzzy Computation
Keyword(s) Fuzzy logic control
hybrid control
impulsive neural networks (INNs)
memristor
multisynchronization
DOI - identifier 10.1109/TFUZZ.2018.2797952
Copyright notice © 2018 IEEE
ISSN 1063-6706
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