Title :
Global Exponential Stability of MAM Neural Network with Time-Varying Delays
Author :
Wang, Ming ; Zhou, Tiejun ; Fang, Haiquan
Author_Institution :
Coll. of Orient Sci. & Technol., Hunan Agric. Univ., Changsha, China
Abstract :
A mathematical model of multidirectional associative memory(MAM) neural networks with varying time delays is proposed. By using Brouwer fixed point theorem, a sufficient condition for the existence of an equilibrium point is obtained. And by constructing a suitable Lyapunov function, a sufficient condition for the global exponential stability of an equilibrium point is obtained, which depends on delays. The results are new for multidirectional associative memory neural networks. An example and its numerical simulation are given to illustrate the effectiveness of the obtained results.
Keywords :
Lyapunov methods; delays; neurocontrollers; time-varying systems; Brouwer fixed point theorem; MAM neural network; global exponential stability; multidirectional associative memory; suitable Lyapunov function; time-varying delays; Artificial neural networks; Associative memory; Biological neural networks; Delay; Neurons; Numerical stability; Stability analysis;
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
DOI :
10.1109/CISE.2010.5676984
