cnns

Therefore , the stability is precondition of credibility work for CNNs .

因此稳定性是细胞神经网络可靠工作的前提。

The primary function of CNNs is to transform an input image into a corresponding output image .

细胞神经网络的主要功能是把一个输入图像转换成一个相应的输出图像。

Theory and applications of the cellular neural networks ( CNNs ) have been a new focus recently .

细胞神经网络（CNNs）的理论和应用研究已经成为了新的研究热点。

We also study the asymptotic behavior of CNNs by means of LaSalle invariant principle .

除此而外，我们还研究了CNN的渐近行为，进行分析的基本工具是稳定性理论中的LaSalle不变原理。

Based on Lyapunov energy function , we establish three sufficient conditions which guarantee global exponential stability of CNNs .

本文应用Lyapunov函数法给出了CNN全局指数稳定的三个充分条件。

Almost Periodic Solutions of Shunting Inhibitory CNNs with Delays ;

具时滞的分路抑制神经网络的概周期解（英文）

Due to these special properties , CNNs have been widely used in artificial intelligence , information security , massive storage , intelligent search and optimization .

这些特殊性质使得混沌神经网络在人工智能、信息安全、海量存储、智能搜索、最优化计算等领域具有重要应用价值。

The mode is based on SSE-CMM ( System Security Engineering - Capability Maturity Model ) and " technology implementing ensuring " pattern advocated by CNNS .

以及基于SSE-CMM（系统安全工程能力成熟度模型）和CNNS所倡导的技术+实施+保证模式的工程实施和保障方案；

For the image processing applications , the implementation of Gradient Vector Flow field and optical flow field by using multiplayer discrete-time CNNs are proposed for image segmentation .

图像处理应用研究主要是利用离散细胞神经网络来实现GVF场和光流场，实现图像分割的目的。

Compared to classical Neural Networks ( NNs ), CNNs have fixed point attractors , periodic attractors and strange attractors .

与经典神经网络不同，混沌神经网络具有不动点吸引子、周期吸引子以及奇怪吸引子。

The paper given some sufficient conditions of complete stability of nonsymmetric cellular neural networks based on plenty of simulation and proves the case of two-cell CNNS .

通过大量的模拟仿真，提出了非对称细胞神经网络完全稳定的充分条件，并就二细胞神经网络的情况给予了证明；

Review the basic concepts , background , state of the art and hardware implementation of CNNs ; and describe the purpose , significance and common technology of image segmentation . 2 .

本文主要工作包括：1.回顾了细胞神经网络的基本概念、背景、发展现状及硬件实现；阐述了图像分割的目的、研究意义、常用技术及本课题研究的意义。

Since cellar neural networks ( CNNs ) was introduced in 1980s by Chua and Yang , this model has received increasing interest due to its promising potential applications in many fields such as pattern recognition , signal processing , parallel computing and combinatorial optimization .

细胞神经网络（CNNs）自从上世纪80年代由Chua和Yang提出以来，它在诸如模式识别、信号处理、并行计算和组合优化等领域有很大的应用。