Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control

The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel an...

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Bibliographic Details
Published in:Neural networks 2016-03, Vol.75, p.162-172
Main Authors: Yang, Shiju, Li, Chuandong, Huang, Tingwen
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Exponential stabilization
Fuzzy
Fuzzy Logic
Fuzzy model of memristive neural networks (MNNs)
Intermittent control
Knowledge
Mathematical models
Models, Theoretical
Neural networks
Neural Networks (Computer)
Nonlinear Dynamics
Resistors
Stabilization
Synchronism
Synchronization
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Summary:The problem of exponential stabilization and synchronization for fuzzy model of memristive neural networks (MNNs) is investigated by using periodically intermittent control in this paper. Based on the knowledge of memristor and recurrent neural network, the model of MNNs is formulated. Some novel and useful stabilization criteria and synchronization conditions are then derived by using the Lyapunov functional and differential inequality techniques. It is worth noting that the methods used in this paper are also applied to fuzzy model for complex networks and general neural networks. Numerical simulations are also provided to verify the effectiveness of theoretical results.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2015.12.003