neural network

neural network的基本解释

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神经式网络

与neural network相关的网络例句 [注：此内容来源于网络，仅供参考]

The proportion of rents that firms get depends on their network capability. This reveals the essential relationships between network resource, network rents and network capability: First of all, network rents come from the network resources. Furthermore, network rent is the representative of the competitive advantage and the value of the network resource. Second, network resource is the foundation of the network capability, the function of network capability is activated in the process of using network resource by firm. It enables the network resource to be a potential value resource of the competitive advantage. At last, network capability activates the network rents in the network resource, and make sure that the firm get the additional benefit;(4) the network capability can be classified as: network visioning capability, network constructing capability, network operating capability and network constructing capability. Some factors, such as maturity of IT, openness of culture, management system involved, experience of participation in network, have a positive effect on the network capability of the firm;(5) the impact of network capability on innovation performance is realized through knowledge transfer between network partners, namely, knowledge transfer is the mediator; and (6) the type of innovation network, exporation network or exploitation network, has moderator effect on the relationship of network capability and knowledge transfer, and network capability and innovation performance as well. In the exploration network, network constructing capability has more effect on the performance of the knowledge transfer and innovation. In the exploitation network, network operating capability has more effect on the performance of the knowledge transfer and innovation.

通过对创新网络环境下网络资源、网络租金和网络能力的概念界定和内涵分析，本研究辨析了三者之间以及它们与竞争优势之间的本质关系：首先，网络资源是网络租金的来源，而网络租金是企业竞争优势的表征，也是网络资源的价值体现；其次，网络资源是网络能力的基础，而网络能力在运用网络资源的过程中发挥其作用，实现了网络资源成为竞争优势源泉的内在价值；最后，网络能力激活了蕴涵在网络资源中的网络租金，并确定企业获得这种额外收益的份额，网络租金正是网络能力发挥作用的成果；(4)企业网络能力可以分成网络规划能力、网络配置能力、网络运作能力和网络占位能力四种，本研究的实证结果表明企业的IT成熟度、文化开放度、网络管理体系和网络活动经验都能够正向影响企业的网络能力水平，因而企业可以通过改善上述各种因素的水平来实现提升企业网络能力的水平；(5)企业网络能力对企业创新绩效的促进作用更多地是通过正向影响知识转移实现的，即知识转移在其中起到了中介作用；(6)创新网络的类型，即探索型创新网络和利用型创新网络，分别在网络配置能力和网络运作能力与知识转移之间的关系中，以及在网络配置能力和网络运作能力与创新绩效之间的关系中起到调节作用，在探索性创新网络中，企业的网络配置能力对提升企业获得的知识转移绩效和创新绩效更为重要；而在利用性创新网络中，企业的网络运作能力对提升企业获得的知识转移绩效和创新绩效更为重要。

In such doing, this dissertation serves as a step stone for papers of its counterparts to come, and, more importantly, it proposes a strategic alternative to the realization of models for image processing. This dissertation consists of three major parts. In the first part, detailed discussions and delicate analyses of academic papers on Cellular Neural Network will be provided in the hope of helping us see the potentiality of Cellular Neural Network in the applications of image processing. I will focus on the aforementioned limitations on hardware compilation as well. In the second part, I will put forth "texture analysis" as one basic model of analysis when we apply Cellular Neural Network to image processing. In this so-called texture analysis, a useful "spatial feature" is especially drawn to help us overcome possible problems of more complicated Cellular Neural Network applications in image processing."Spatial feature" also serves as a well-functioning mechanism for technology of image identification. In the last part of this thesis, I will look into a case study, where Cellular Neural Network is applied to help de-screen document image. Using it as an example, we will see how algorithms of Cellular Neural Network may be of marvelous use in applications in document image processing, since it would reduce a great deal of calculation and computation when applied to software compilation, yet opens up unlimited possibilities for higher-speed hardware compilation of high-level image processing.

这篇论文主要可以分为三大部分：在第一部份里，我们会详细地说明并讨论在过去到现在大部分将分子类神经网路应用於影像处理的相关文献及未来所有可能的发展和技术，另外也将分子类神经网路作一完整的介绍，除此之外，我们也会特别著重於分子类神经网路在影像处理相关应用理论的讨论以及其硬体实现化的考量；在第二部分里，我们提出了一个将分子类神经网路应用於影像辨识处理的基础分析—纹路分析，这是由於纹路分析的复杂性和普遍性会使得分子类神经网路於高阶影像处理的应用不会只局限在单一的影像处理技术，其中我们也提出了一个相当有用的空间特徵，此一特徵不但可以使复杂地高阶影像处理能够应用分子类神经网路，也为影像辨识技术提供了一个很好的辨识机制；在最后一部分里，我们也将文件影像分析做了一个完整的剖析，并以文件影像的去网点为例来说明在实际情况下的分子类神经网路的应用，如此演算法的开发也为文件影像处理提供了更多实际的应用，更考量了文件影像处理若以软体实现时的计算量负荷，而对未来高阶数位影像处理能够以硬体实现来提高处理速度提供了无限的可能。

objective the aim of this study is to investigate the expression and the distribution of the nerve growth factor during the period of neural tube development of human embryo.method early development of neural tube was studied in human embryos about 35 gestational days by using immunocytochemical abc technique.result there were ngf immuno-positive substances in the cytoplasm and nuclei of neuroepithelial cells in the ventricular zones of neural tube.in the intermediate zone of neural tube,ngf immunoreactivity was detected in the nuclei of some neurons,or the processes of other neurons which contained no ngf-immunoreactive substances in their nuclei;the expression pattern of ngf in the marginal zone of neural tube was similar to that of the intermediate zone.the density of ngf-immunorecative particles was higher on the rostrum side of neural tube than on the caudal side.the ngf immuno-positive cells were also observed among the somites of embryo under the neural tube.conclusion these results suggest that ngf was an important signal molecule to induce neural tube differentiation,and that ngf may play a significant role in regulation of the biological function of neurons in developing neural tube.

目的 研究捷安肽素的抗真菌作用机理。方法采用形态学方法和同位素标记法。显微形态观察经捷安肽素处理后的供试真菌的形态学变化。进一步采用14c同位素标记的特异底物&尿苷二磷酸-（14c）-葡萄糖&示踪，研究捷安肽素对真菌(1，3)-β-d-葡聚糖合成酶活性反应的影响。结果研究神经生长因子在早期人胚神经管发育过程中的定位表达。方法采用免疫细胞化学 abc法染色，研究35天人胚的发育情况。结果在人胚神经管的室管带中，神经元的细胞质和细胞核ngf免疫反应阳性；在中间带，一部分神经元的细胞核ngf免疫反应阳性，另外一部分神经元的细胞核ngf免疫反应阴性，而其突起ngf免疫反应阳性；在边缘带ngf的表达与中间带相似。在神经管的头侧ngf阳性反应较强，神经管的尾侧ngf阳性反应较弱。结论 ngf在人胚神经管免疫反应阳性，表明ngf可能是诱导神经管分化发育的重要信号分子，提示ngf可能在人胚神经管的发育中具有十分重要的作用。神经生长因子；人胚；神经管；发育

与neural network相关的网络解释 [注：此内容来源于网络，仅供参考]

Neural groove,Medullary groove：神经沟,髓沟

神经褶,髓褶 Neural fold,Medullary fold,Neural ridge | 神经沟,髓沟 Neural groove,Medullary groove | 神经褶,髓褶 Neural ridge,Neural fold,Medullary fold

lateral mesoderm：侧位中层

侧神经褶 Lateral medullary fold,Lateral neural ridge,lateral neural fold | 侧位中层 Lateral mesoderm | 侧神经褶 Lateral neural fold,Lateral neural ridge,Lateral medullary fold

neural：神经元

如此反覆的运作直到剩下唯一与平均GEMNET 是将神经元(neural)减掉所有神经元(neural)之平均值;而 IGEMNET(IMPROVED GEMNET )则是神经元(neural)扣掉本身神经元之剩余神经元断反覆收敛直到找出最大活化神经元(neural)为止.