Stability and bifurcation in a simplified four-neuron bam neural network with multiple delays.
Yan, Xiang-Ping, Li, Wan-Tong (2006)
Discrete Dynamics in Nature and Society
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Yan, Xiang-Ping, Li, Wan-Tong (2006)
Discrete Dynamics in Nature and Society
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Ren, Yaping, Li, Yongkun (2007)
International Journal of Mathematics and Mathematical Sciences
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Liu, Qiming, Xu, Rui, Wang, Zhiping (2011)
Discrete Dynamics in Nature and Society
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Zhou, Xiaobing, Jiang, Murong, Cai, Xiaomei (2011)
Discrete Dynamics in Nature and Society
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Anita Windisch, Péter L. Simon (2023)
Applications of Mathematics
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The dynamical behaviour of a continuous time recurrent neural network model with a special weight matrix is studied. The network contains several identical excitatory neurons and a single inhibitory one. This special construction enables us to reduce the dimension of the system and then fully characterize the local and global codimension-one bifurcations. It is shown that besides saddle-node and Andronov-Hopf bifurcations, homoclinic and cycle fold bifurcations may occur. These bifurcation...
Qu, Ying, Wei, Junjie (2010)
Discrete Dynamics in Nature and Society
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Jalab, H.A., Ibrahim, R.W. (2009)
Surveys in Mathematics and its Applications
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Wang, Yi, Ma, Zhongjun, Liu, Hongli (2009)
Journal of Inequalities and Applications [electronic only]
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Bai, C., Li, C. (2010)
Surveys in Mathematics and its Applications
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Lan Zhang, Cheng Jian Zhang (2008)
Kybernetika
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A four-dimensional hyperchaotic Lü system with multiple time-delay controllers is considered in this paper. Based on the theory of Hopf bifurcation in delay system, we obtain a simple relationship between the parameters when the system has a periodic solution. Numerical simulations show that the assumption is a rational condition, choosing parameter in the determined region can control hyperchaotic Lü system well, the chaotic state is transformed to the periodic orbit. Finally, we consider...
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.