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
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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.
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
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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
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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
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Dynamical analysis of DTNN with impulsive effect.
Chen, Chao, Huang, Zhenkun, Bin, Honghua, Liu, Xiaohui (2009)
Discrete Dynamics in Nature and Society
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Exponential convergence of shunting inhibitory cellular neural networks with continuously distributed delays
Qiyuan Zhou, Changhong Zhao (2010)
Annales Polonici Mathematici
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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
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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,...
Dynamic properties of cellular neural networks.
Slavova, Angela (1993)
Journal of Applied Mathematics and Stochastic Analysis
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On Popov-type stability criteria for neural networks.
Danciu, Daniela, Răsvan, Vladimir (2000)
Electronic Journal of Qualitative Theory of Differential Equations [electronic only]
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Boundedness and stability for discrete-time delayed neural network with complex-valued linear threshold neurons.
Duan, Chengjun, Song, Qiankun (2010)
Discrete Dynamics in Nature and Society
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Stability of stochastic reaction-diffusion recurrent neural networks with unbounded distributed delays.
Huang, Chuangxia, Yang, Xinsong, He, Yigang, Huang, Lehua (2011)
Discrete Dynamics in Nature and Society
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Mean square exponential stability of stochastic Cohen-Grossberg neural networks with unbounded distributed delays.
Huang, Chuangxia, Huang, Lehua, He, Yigang (2010)
Discrete Dynamics in Nature and Society
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Delay dependent complex-valued bidirectional associative memory neural networks with stochastic and impulsive effects: An exponential stability approach
Chinnamuniyandi Maharajan, Chandran Sowmiya, Changjin Xu (2024)
Kybernetika
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This paper investigates the stability in an exponential sense of complex-valued Bidirectional Associative Memory (BAM) neural networks with time delays under the stochastic and impulsive effects. By utilizing the contracting mapping theorem, the existence and uniqueness of the equilibrium point for the proposed complex-valued neural networks are verified. Moreover, based on the Lyapunov - Krasovskii functional construction, matrix inequality techniques and stability theory, some novel...