Global Stability of Almost Periodic Solution of a Class of Neutral-Type BAM Neural Networks | Zendy

Tetie Pan | Zendy; Ke Shi | Zendy; Jian Yuan | Zendy

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Global Stability of Almost Periodic Solution of a Class of Neutral-Type BAM Neural Networks

Author(s) -

Tetie Pan,

Ke Shi,

Jian Yuan

Publication year - 2012

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2012/482584

Subject(s) - mathematics , exponential dichotomy , exponential stability , class (philosophy) , artificial neural network , type (biology) , stability (learning theory) , fixed point theorem , fixed point , variable (mathematics) , exponential function , mathematical analysis , control theory (sociology) , differential equation , pure mathematics , nonlinear system , computer science , control (management) , ecology , physics , machine learning , biology , quantum mechanics , artificial intelligence

A class of BAM neural networks with variable coefficients and neutral delays are investigated. By employing fixed-point theorem, the exponential dichotomy, and differential inequality techniques, we obtain some sufficient conditions to insure the existence and globally exponential stability of almost periodic solution. This is the first time to investigate the almost periodic solution of the BAM neutral neural network and the results of this paper are new, and they extend previously known results

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