Exponential stability and periodicity of memristor-based recurrent neural networks with time-varying delays Academic Article

abstract

• In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.

authors

• Huang, Tingwen

published proceedings

• INTERNATIONAL JOURNAL OF BIOMATHEMATICS

author list (cited authors)

• Zhang, W., Li, C., & Huang, T.

citation count

• 1

complete list of authors

• Zhang, Wei||Li, Chuandong||Huang, Tingwen

publication date

• February 2017

publisher

• World Scientific Publishing  Publisher

published in

• International Journal of Biomathematics  Journal

keywords

• Exponential Stability
• Linear Matrix Inequality (lmi)
• Periodicity
• Recurrent Neural Networks

Digital Object Identifier (DOI)

• 10.1142/S1793524517500279

start page

• 1750027

end page

• 1750027

volume

• 10

issue

• 2

URL

• http://dx.doi.org/10.1142/s1793524517500279