Results 141 to 150 of about 438 (172)

Qualitative analysis of Cohen-Grossberg neural networks with multiple delays

Physical Review E, 1995
It is well known that a class of artificial neural networks with symmetric interconnections and without transmission delays, known as Cohen-Grossberg neural networks, possesses global stability (i.e., all trajectories tend to some equilibrium). We demonstrate in the present paper that many of the qualitative properties of Cohen-Grossberg networks will ...
, Ye, , Michel, , Wang
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Hopf bifurcation in Cohen–Grossberg neural network with distributed delays

Nonlinear Analysis: Real World Applications, 2007
The authors of this interesting paper discuss stability and bifurcation of distributed delays Cohen-Grossberg neural networks with two neurons. By choosing the average delay as a bifurcation parameter, they prove that Hopf bifurcation occurs. They also determine the stability of bifurcating periodic solutions and the direction of Hopf bifurcation by ...
Zhao, Hongyong, Wang, Lei
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A further analysis on harmless delays in Cohen–Grossberg neural networks

Chaos, Solitons & Fractals, 2007
The paper is concerned with the global exponential stability of the unique equilibrium for the Cohen-Grossberg neural network model with multiple discrete time delays and constant external inputs: \[ x_i'=-a_i(x_i)\biggl(b_i(x_i)- \sum_{k=0}^K\sum_{j=1}^n t_{ij}^{(k)}s_j(x_j(t-\tau_k))+J_i\biggr),\quad i=1,2,\dots,n.
Li, Chun-Hsien, Yang, Suh-Yuh
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Global exponential stability of delayed cohen-grossberg neural networks

ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004., 2005
In this paper, using a fixed-point theorem and reduction to absurdity, the authors have obtained some sufficient conditions to guarantee that Cohen-Grossberg neural networks with discrete and distributed delays are globally exponentially stable. Since the model is more general and the assumptions relax the previous assumptions in some existing works ...
Zhigang Zeng, Zengfu Wang, De-Shuang Huang   +2 more
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Global asymptotic stability of delayed Cohen–Grossberg neural networks

Chaos, Solitons & Fractals, 2007
The authors consider a class of Cohen-Grossberg neural networks given by the delayed dynamical system \[ {dx_i(t)\over dt}= -a_i(x_i(t))\Biggl[b_i(x_i(t))- \sum^n_{j=1} a_{ij} f_j(x_j(t))- \sum^n_{j=1} a^\tau_{ij} f_j(x_j(t- \tau_j(t)))+ I_i\Biggr], \] \(i= 1,2,\dots, n\), where \(A= (a_{ij})_{n\times n}\) and \(A^\tau= (a^\tau_{ij})_{n\times n}\) are ...
Wu, Wei, Cui, Bao Tong, Huang, Min
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Dynamics of Cohen–Grossberg neural networks with variable and distributed delays

Physica D: Nonlinear Phenomena, 2007
Cohen-Grossberg neural networks with variable and distributed delays are studied. Sufficient conditions for existence, uniqueness and exponential stability of an equilibrium point are proved by means of Brouwer's fixed-point theorem and inequality technique. Numerical examples are provided by the authors as well.
Mao, Zisen, Zhao, Hongyong, Wang, Xiaofeng   +2 more
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Periodic Solution of Cohen-Grossberg Neural Networks with Variable Coefficients

2007
In this paper, the periodic solution for a class of Cohen-Grossberg neural networks with variable coefficients is discussed. By using inequality analysis technique and matrix theory, some new sufficient conditions are obtained to ensure the existence, uniqueness, global attractivity and exponential stability of the periodic solution.
Hongjun Xiang, Jinde Cao
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Exponential Stability of Cohen-Grossberg Neural Networks with Delays and Impulses

2009 International Conference on Artificial Intelligence and Computational Intelligence, 2009
As an important tool to study practical problems of biology, engineering and image processing, the neural networks has caused more and more attention. Some interesting results on the stability have been obtained. In this paper, the exponential stability of the equilibrium point of a group of Cohen-Grossberg neural networks is obtained by using Lyapunov
null Qing Tang, Anping Liu, null Huijuan Li, Ting Liu   +3 more
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Attractor and boundedness for stochastic Cohen–Grossberg neural networks with delays

Neurocomputing, 2012
By employing Lyapunov method and Lasalle-type theorem, the attractor of stochastic Cohen-Grossberg neural networks (CGNN) with delays is initially investigated. Novel results and sufficient criteria on the attractor of stochastic CGNN are obtained. The almost surely asymptotic stability is a special case of our results.
Li Wan 0003, Qinghua Zhou 0001
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Weak attractor for stochastic Cohen–Grossberg neural networks with delays

Nonlinear Dynamics, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wan, Li, Zhou, Qinghua, Wang, Pei
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