Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays | Zendy

Haiyong Zheng | Zendy; Bin Wu | Zendy; Tengda Wei | Zendy; Linshan Wang | Zendy; Yangfan Wang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

Author(s) -

Haiyong Zheng,

Bin Wu,

Tengda Wei,

Linshan Wang,

Yangfan Wang

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/705496

Subject(s) - exponential stability , artificial neural network , control theory (sociology) , computer science , stability (learning theory) , lyapunov function , type (biology) , hopfield network , mathematical optimization , mathematics , artificial intelligence , machine learning , nonlinear system , control (management) , ecology , physics , quantum mechanics , biology

By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the high-order neural networks with S-type distributed time delays are established, which are easy to be verified with a wider adaptive scope

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research