Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays. Academic Article

abstract

• This paper deals with the problem of global exponential synchronization of a class of memristor-based recurrent neural networks with time-varying delays based on the fuzzy theory and Lyapunov method. First, a memristor-based recurrent neural network is designed. Then, considering the state-dependent properties of the memristor, a new fuzzy model employing parallel distributed compensation (PDC) gives a new way to analyze the complicated memristor-based neural networks with only two subsystems. Comparisons between results in this paper and in the previous ones have been made. They show that the results in this paper improve and generalized the results derived in the previous literature. An example is also given to illustrate the effectiveness of the results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Wen, S., Bao, G., Zeng, Z., Chen, Y., & Huang, T.

citation count

• 165

complete list of authors

• Wen, Shiping||Bao, Gang||Zeng, Zhigang||Chen, Yiran||Huang, Tingwen

publication date

• December 2013

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Artificial Intelligence
• Computer Simulation
• Fuzzy Logic
• Memristor
• Models, Neurological
• Neural Networks, Computer
• Nonlinear Dynamics
• Recurrent Neural Networks
• Synchronization
• Time-varying Delays

PubMed Central ID

• 24216502

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2013.10.001

start page

• 195

end page

• 203

volume

• 48

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neunet.2013.10.001