针对多层细胞神经网络模型，分析了其一般状态解并对其动力值域作了估计。
Utilizing the theory of the Drazin inverse, we studied the expression of the general state solution of Multi-layer Cellular Neural Networks.
第三章对变系数离散时间混合时滞细胞神经网络模型周期解的存在性与全局指数稳定性进行了讨论。
In Chapter 3, we discuss the existence and global exponential stability of periodic solutions for discrete-time cellular neural network with mixed delays and variable coefficients.
现在的大脑模型仅仅能够模拟拥有数千个神经元细胞的神经网络，但是这种状况正迅速被改写。
Brain modelers have so far been limited to modeling small networks with only a few thousand neurons, but this is rapidly changing.
本文主要研究了细胞神经网络无时滞和有时滞的几类模型的稳定性和基于离散细胞神经网络在图像处理方面的应用。
In this paper, some theorems of stability for several kinds of CNNs are proved and applications in image processing are also obtained.
本文主要研究了细胞神经网络无时滞和有时滞的几类模型的稳定性和基于离散细胞神经网络在图像处理方面的应用。
In this paper, some theorems of stability for several kinds of CNNs are proved and applications in image processing are also obtained.
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