Details

Autor(en) / Beteiligte

• Shu, Yan-Jun
• Liu, Xin-Ge
• Wang, Feng-Xian
• Qiu, Sai-Bing

Titel

Exponential input-to-state stability of stochastic neural networks with mixed delays

Ist Teil von

• International journal of machine learning and cybernetics, 2018-05, Vol.9 (5), p.807-819

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This paper is concerned with the exponential input-to-state stability for a class of stochastic neural networks with mixed delays. Based on a new Lyapunov-Krasovskii functional, Itô’s formula, Dynkin’s formula and some inequality techniques, some novel sufficient conditions ensuring the exponential input-to-state stability in the mean square for the given stochastic neural networks are derived. Some existing results are extended. Two numerical examples are provided to illustrate the effectiveness of the proposed method.