Periodic Oscillation of Fuzzy Cohen‐Grossberg Neural Networks with Distributed Delay and Variable Coefficients | Zendy

Hongjun Xiang | Zendy; Jinde Cao | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Periodic Oscillation of Fuzzy Cohen‐Grossberg Neural Networks with Distributed Delay and Variable Coefficients

Author(s) -

Hongjun Xiang,

Jinde Cao

Publication year - 2008

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 - 10.1155/2008/453627

Subject(s) - uniqueness , coincidence , mathematics , artificial neural network , fuzzy logic , variable (mathematics) , exponential stability , class (philosophy) , oscillation (cell signaling) , control theory (sociology) , stability (learning theory) , computer science , mathematical analysis , nonlinear system , control (management) , artificial intelligence , physics , quantum mechanics , machine learning , biology , genetics , medicine , alternative medicine , pathology

A class of fuzzy Cohen-Grossberg neural networks with distributed delay and variable coefficients is discussed. It is neither employing coincidence degree theory nor constructing Lyapunov functionals, instead, by applying matrix theory and inequality analysis, some sufficient conditions are obtained to ensure the existence, uniqueness, global attractivity and global exponential stability of the periodic solution for the fuzzy Cohen-Grossberg neural networks. The method is very concise and practical. Moreover, two examples are posed to illustrate the effectiveness of our results

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