Dynamics of a class of discete-time neural networks and their comtinuous-time counterparts
Dynamics of a class of discete-time neural networks and their comtinuous-time counterparts
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DOI：
10.1016/s0378-4754(00)00168-3
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发表时间：
2000-08
影响因子：
4.6
通讯作者：
S. Mohamad;K. Gopalsamy
中科院分区：
文献类型：
--
作者：
S. Mohamad;K. Gopalsamy
研究了连续时间加性Hopfield型神经网络的动力学特性。得到了对时间均匀的外部刺激进行指数稳定编码的充分条件。建立了相应的连续时间模型的离散时间模拟，解析地证明了网络的动态被连续时间系统和离散时间系统所保持。本研究得出两个主要结论：第一，证明了所提出的离散时间模拟作为数学模型在离散时间内稳定地编码与外部刺激相关的联想记忆的适用性；第二，证明了我们的离散时间模拟作为数值算法在模拟连续时间网络中的适用性。
The dynamical characteristics of continuous-time additive Hopfield-type neural networks are studied. Sufficient conditions are obtained for exponentially stable encoding of temporally uniform external stimuli. Discrete-time analogues of the corresponding continuous-time models are formulated and it is shown analytically that the dynamics of the networks are preserved by both continuous-time and discrete-time systems. Two major conclusions are drawn from this study: firstly, it demonstrates the suitability of the formulated discrete-time analogues as mathematical models for stable encoding of associative memories associated with external stimuli in discrete time, and secondly, it illustrates the suitability of our discrete-time analogues as numerical algorithms in simulating the continuous-time networks.