Passivity analysis of delayed reaction-diffusion memristor-based neural networks. Academic Article

abstract

• This paper discusses the passivity of delayed reaction-diffusion memristor-based neural networks (RDMNNs). By exploiting inequality techniques and by constructing appropriate Lyapunov functional, several sufficient conditions are obtained in the form of linear matrix inequalities (LMIs), which can be used to ascertain the passivity, output and input strict passivity of delayed RDMNNs. In addition, the passivity of RDMNNs without any delay is also considered. These conditions, represented by LMIs, can be easily verified by virtue of the Matlab toolbox. Finally, some illustrative examples are provided to substantiate the effectiveness and validity of the theoretical results, and to present an application of RDMNN in pseudo-random number generation.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Cao, Y., Cao, Y., Wen, S., Huang, T., & Zeng, Z.

citation count

• 82

complete list of authors

• Cao, Yanyi||Cao, Yuting||Wen, Shiping||Huang, Tingwen||Zeng, Zhigang

publication date

• January 2019

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Algorithms
• Diffusion
• Memristor
• Neural Network
• Neural Networks, Computer
• Passivity

PubMed Central ID

• 30445346

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2018.10.004

start page

• 159

end page

• 167

volume

• 109

URL

• http://dx.doi.org/10.1016/j.neunet.2018.10.004