Passivity analysis of memristor-based recurrent neural networks with time-varying delays Academic Article

abstract

• This paper investigates the delay-dependent exponential passivity problem of the memristor-based recurrent neural networks (RNNs). Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based RNNs is established. Taking into account of the information of the neuron activation functions and the involved time-varying delays, several improved results with less computational burden and conservatism have been obtained in the sense of Filippov solutions. A numerical example is presented to show the effectiveness of the obtained results. 2013 The Franklin Institute.

authors

• Huang, Tingwen

published proceedings

• JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS

author list (cited authors)

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

citation count

• 81

complete list of authors

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

publication date

• October 2013

publisher

• Elsevier  Publisher

published in

• Journal of the Franklin Institute  Journal

Digital Object Identifier (DOI)

• 10.1016/j.jfranklin.2013.05.026

start page

• 2354

end page

• 2370

volume

• 350

issue

• 8

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.jfranklin.2013.05.026