Global mean square exponential stability of stochastic neural networks with retarded and advanced argument Academic Article

abstract

• 2017 Elsevier B.V. This paper focuses on the global mean square exponential stability of stochastic neural networks with retarded and advanced argument. By employing the theory of differential equations with piecewise constant argument of generalized type, several sufficient conditions in form of algebraic inequalities are proposed to ensure the existence and uniqueness of solution. Considering that the piecewise alternately retarded and advanced argument exists, we estimate dynamic effect of system status in the current time and in the deviating function. Theoretical analysis of global mean square exponential stability is carried out by the stability theory of stochastic differential equations. Finally, numerical examples are exploited to illustrate the effectiveness of the results established.

authors

• Huang, Tingwen

published proceedings

• NEUROCOMPUTING

author list (cited authors)

• Liu, L., Wu, A., Zeng, Z., & Huang, T.

citation count

• 13

complete list of authors

• Liu, Ling||Wu, Ailong||Zeng, Zhigang||Huang, Tingwen

publication date

• July 2017

publisher

• Elsevier  Publisher

published in

• Neurocomputing  Journal

keywords

• Mean Square Exponential Stability
• Retarded And Advanced Argument
• Stochastic Neural Networks

Digital Object Identifier (DOI)

• 10.1016/j.neucom.2017.03.057

start page

• 156

end page

• 164

volume

• 247

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.neucom.2017.03.057