Almost-periodic solution for BAM neural networks.

Jalab, H.A.; Ibrahim, R.W.

Displaying similar documents to “Almost-periodic solution for BAM neural networks.”

New result of existence of periodic solution for a Hopfield neural networks with neutral time-varying delays.

Bai, C., Li, C. (2010)

Surveys in Mathematics and its Applications

Similarity:

Stability analysis and absolute synchronization of a three-unit delayed neural network

Lin Jun Wang, You Xiang Xie, Zhou Chao Wei, Jian Peng (2015)

Kybernetika

Similarity:

In this paper, we consider a three-unit delayed neural network system, investigate the linear stability, and obtain some sufficient conditions ensuring the absolute synchronization of the system by the Lyapunov function. Numerical simulations show that the theoretically predicted results are in excellent agreement with the numerically observed behavior.

Delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays.

Chen, Yonggang, Bi, Weiping, Wu, Yuanyuan (2008)

Discrete Dynamics in Nature and Society

Similarity:

On global exponential stability of discrete-time Hopfield neural networks with variable delays.

Zhang, Qiang, Wei, Xiaopeng, Xu, Jin (2007)

Discrete Dynamics in Nature and Society

Similarity:

Dynamical analysis of DTNN with impulsive effect.

Chen, Chao, Huang, Zhenkun, Bin, Honghua, Liu, Xiaohui (2009)

Discrete Dynamics in Nature and Society

Similarity:

Exponential convergence of shunting inhibitory cellular neural networks with continuously distributed delays

Qiyuan Zhou, Changhong Zhao (2010)

Annales Polonici Mathematici

Similarity:

We study delay shunting inhibitory cellular neural networks without almost periodic coefficients. Some sufficient conditions are established to ensure that all solutions of the networks converge exponentially to an almost periodic function. This complements previously known results.

Local stability conditions for discrete-time cascade locally recurrent neural networks

Krzysztof Patan (2010)

International Journal of Applied Mathematics and Computer Science

Similarity:

The paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover,...