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

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Yang, S., Li, C., & Huang, T.

citation count

• 126

complete list of authors

• Yang, Shiju||Li, Chuandong||Huang, Tingwen

publication date

• March 2016

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Exponential Stabilization
• Fuzzy Logic
• Fuzzy Model Of Memristive Neural Networks (mnns)
• Intermittent Control
• Knowledge
• Models, Theoretical
• Neural Networks, Computer
• Nonlinear Dynamics
• Synchronization

PubMed Central ID

• 26797471

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2015.12.003

start page

• 162

end page

• 172

volume

• 75

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neunet.2015.12.003