类演算

词组短语

calculus of classes

与类演算相关的网络例句 [注：此内容来源于网络，仅供参考]

The sum of algebraic rewriting system and second order λ-calculus can be used as an operational model of FOPL.

对一个类定义，通过限制方程的从左向右使用即可得到一个代数重写系统，代数重写系统和二阶λ演算的和即可作为FOPL的一个操作模型。

Tarski's definition of truth of the calculus of classes is: x is true if and only if x is a sentence of the calculus of classes and every infinite sequence of classes satisfies x .

塔斯基得到的真概念定义是：x是类演算语言的任一语句，x为真当且仅当每一个类的无穷序列都满足x。

After analyzing the structure of sentences,Tarski found that, it was impossible to define the truth of the calculus of classes directly,but making use of the structure character of sentential functions could give us the approach to escape the dilemma:Firstly,we could defined the concept of satisfaction in sentential functions by means of recursive method;Then,we could regard sentences as a peculiar form of sentential functions;Finally,with the help of the concept of satisfaction,we could defined successfully the truth of the calculus of classes.

通过分析语句的结构，塔斯基发现很难直接定义出类演算语言的真概念，而语句函项所具有的可递归形成的结构性质可以使我们得到脱离困境的办法：先采用递归方法针对语句函项定义出满足概念，然后把语句作为语句函项的特殊形式，借助于满足概念最终可以得到类演算语言的真概念定义。

Tarski constructed strictly the calculus of classes and its metalanguage,then defined some important concepts of the calculus of classes,such as sentential function,free variable, sentence,operation,consequence.

塔斯基严格地构造出了类演算语言，然后又构造出了类演算语言的元语言，并且在元语言中定义出类演算语言的语句函项、自由变元、语句、运算、后承等重要概念。

However, the conventional CMAC has an enormous memory requirement so that it cannot be applied to solve higher dimensional problems.

因此，本研究提出一个具有自组织能力之阶层式小脑模型类神经网路，它可以依据分析学习样本的分布，自动建立网路的记忆体配置，并藉由阶层式的学习架构，来有效降低记忆体的需求，使其具有解决高维度问题的能力，而此类神经网路架构亦具有可扩充性，可以在不影响其他网路的学习架构下，任意的增减输出节点的个数，此外，我们也提出了一个渐进式学习的演算法来训练此一类神经网路。

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。

Furthermore, the advantages of the genetic algorithm and the above single layer neural network are combined to yield a new identification technique. The network topology is employed to replace the procedure for solving the governing equation when GA is used to identify the system parameters of both the SDOF system and the MDOF system with or without noise contamination. The comparison is made between the predicted acceleration and the measured one for each case.

另外，结合类神经网路与基因演算法於结构动力参数识别，首先应用前述之单层类神经网路架构做为系统动力模式，再藉由基因演算法搜索符合该系统之权重值，然后将权重值代入网路架构以取代解微分方程式求得系统反应，最后再与量测反应做比较；同时亦利用该方法应用於进行识别含杂讯之输出入资料的系统权重值，以验证该方法应用於单自由度线性系统及多自由度线性系统识别之可行性。

In SPVT (security protocol verifying tool), the specification language is the π-like calculus extended with three appendixes, the Dolev-Yao model is described with Horn logic rules, the π-like calculus model of security protocol is transformed into the logic program model by abstract rules, the security properties are verified based on the calculus of the logic program's fixpoint, and the counter-examples on security properties are constructed from the process of the fixpoint calculus and the process of the property verification.

在SPVT中，以扩展附加项的类演算作为安全协议描述语言，以扩展附加项的Horn逻辑规则描述协议攻击者的Dolev-Yao模型，通过一组抽象规则将安全协议的类(演算模型转换为逻辑程序模型，基于安全协议逻辑程序的不动点计算验证安全性质，从安全协议逻辑程序的不动点计算和安全性质的验证过程中构造不满足安全性质的安全协议反例。

From the starting value selection method in the Fuzzy ISODATA algorithm, used the method of maximal matrix element to ascertain the number of classification, the theoretical analysis of repeated test, and finally, the improved fuzzy ISODATA algorithm is obtained. The algorithm reduced sensitivity to the starting value. The algorithm can highly effective clustering analyze and obtains a stable result, so it presents an efficient way of improving the contribution value of custom service.

研究中先对模糊ISODATA聚类演算法中，初始划分矩阵和分类数的确定，并使用最大矩阵元素法，求得最佳化演算法中其他参数值，最后在CRM系统实际验证和分析中，采用本研究改良之模糊ISODATA聚类演算法，对汽车销售公司实施客户模组分类，经实验证明所得到的聚类结果，可有效解决一般聚类方法受限於参数设定敏感度的困扰，并使聚类精确度提高，有效排除杂讯敏感的影响，使聚类效果大幅提升，可帮助行销人员做出预测，制订出针对客户差异化的行销策略，提高客户服务的贡献价值。

We choose back-propagation neural network model for studying. In this paper we take RMR rockmass classification data set from the Fuder Tunnel for studying.

本文选择类神经网路中，倒传递类神经网路( Back-Propagation Neural Network )演算模式来进行研究。

与类演算相关的网络解释 [注：此内容来源于网络，仅供参考]

calculus of approximations：近似计算

calculus 演算 | calculus of approximations 近似计算 | calculus of classes 类演算

calculus of classes：类演算

如命题演算(Calculus of proposition)、类演算(Calculus of classes)等. 此外"Calculus"一词还用来表示"微积分学". 计算机或计算器本身则被称之为"Calculator". 克、克拉与盎司都是质量(重量)的计量单位. "克拉(carat)一词来自希腊文,

calculus of errors：误差论

calculus of classes 类演算 | calculus of errors 误差论 | calculus of finite differences 差分法

immediate derived：直接下层派生类

immediate base 直接上层基类 | immediate derived 直接下层派生类 | immediate rendering 立即演算上色