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1.
徐春选 《数字社区&智能家居》2009,(12)
排序是C语言教学中经常碰到的内容,其方法有很多,常用的有三种:交换排序法、选择排序和冒泡排序等。对这三种方法用C语言进行详细分析,以便初学者能够更好的理解和应用。 相似文献
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本文较详细地介绍在开发 MIS 中快速原型法的概念、主要内容及实际应用过程,并与软件工程法进行了分析比较。探讨了在实际工作中应用快速原型法所应处理的问题。从而在我们开发 MIS 时工作更有成效。 相似文献
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雷藏民 《计算机光盘软件与应用》2012,(13):237-238
本文主要从行动导向法对于教学实践活动的主要影响、行动导向法的具体教学实践方法以及行动导向法在计算机数据库教学实践中的应用等内容进行论述。 相似文献
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企业CIMS/MIS开发的一种新型方法——混和原形法 总被引:5,自引:0,他引:5
徐宝民 《计算机工程与应用》1998,34(4):57-59
本文在电力企业MIS开发的调查研究和实践的基础上,首次提出用BSP(Businessystemprograming)法与原形法(Prototyping)相结合的混和原型开发方式,并在实践中进行了成功的应用,从而为系统开发方法的宝库增添了新的内容。 相似文献
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成本管理是项目管理的重要内容之一,选择合适的成本管理方法对于项目的成本控制至关重要。通过比较分析目前流行的挣值法、项目分解结构法和功能点估算法,分别从项目的过程监控和规模估算等角度,提出了各个方法的适用范围以及优缺点,意图对项目成本的管理提供参考借鉴。 相似文献
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随着对合理、经济利用能源、资源要求的不断提高,换热网络综合引起了人们的高度重视。本文对过程工业非常重要的过程集成问题——换热网络综合,总结了过程热集成常见的3种方法:夹点设计法、数学规划法以及人工智能法。并分别对这3种方法在发展过程中的研究内容和设计方法及其取得的研究成果进行了讨论。最后从工业应用角度对不同的方法做了比较及评价。 相似文献
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目前针对法条预测的相关研究大都采用文本分类的思想,但模型构建过程都未考虑不同法条之间的从属关系或相似程度,因此对于易混淆法条预测效果普遍较差。针对现有方法在易混淆法条预测中存在的不足,提出基于分层学习的易混淆法条预测方法。将法条分为易区分法条和易混淆法条,按法条内容将易混淆法条组合为不同易混淆法条集并分别训练易混淆法条集预测模型,运用分层学习完成易混淆法条预测。在刑事案件的数据上进行实验,实验结果表明,该模型能较好解决易混淆法条预测问题,提高法条预测准确率。 相似文献
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Nickolay T. Trendafilov Ian T. Jolliffe 《Computational statistics & data analysis》2007,51(8):3718-3736
The objective of DALASS is to simplify the interpretation of Fisher's discriminant function coefficients. The DALASS problem—discriminant analysis (DA) modified so that the canonical variates satisfy the LASSO constraint—is formulated as a dynamical system on the unit sphere. Both standard and orthogonal canonical variates are considered. The globally convergent continuous-time algorithms are illustrated numerically and applied to some well-known data sets. 相似文献
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This paper considers the problem of estimating the probability of misclassifying normal variates using the usual discriminant function when the parameters are unknown. The probability of misclassification is estimated, by Monte Carlo simulation, as a function of n1 and n2 (sample sizes), p (number of variates) and α (measure of separation between the two populations). The probability of misclassification is used to determine, for a given situation, the best number and subset of variates for various sample sizes. An example using real data is given. 相似文献
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模式识别催化剂生产调优 总被引:2,自引:0,他引:2
王景芳 《计算机与应用化学》1992,9(4):260-266
本文介绍如何将模式识别应用于生产调优,着重讨论了多指标(优化目标)因素的分级,提出了用模糊综合评价度量和依有序聚类最小损失函数准则划类,并相继进行变量与样本筛选、信息压缩、特征提取和模拟仿真获得优区操作条件,实施效果显著。 相似文献
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M. E. JAKUBAUSKAS 《International journal of remote sensing》2013,34(12):2323-2332
Canonical correlation analysis was used to examine the relations between the six reflective Thematic Mapper bands and six forest structural variables for 70 lodgepole pine forest stands in Yellowstone National Park, U.S.A. Two significant canonical variate pairs were extracted, accounting for 96·4 per cent of the total information in the overall canonical correlation analysis. Results of the canonical redundancy analysis indicate that 78 per cent of the overall unstandardized variance in spectral data is explained by the first two spectral canonical variates, while the first and second biotic canonical variates explain 59 per cent and 5·9 per cent of the raw variance in the spectral data. The first two biotic canonical variates collectively explain 59 per cent of the raw variance in the biotic data, and the first and second spectral canonical variates explain 41 per cent and 6 per cent of the raw variance in the biotic data, respectively. Height, live basal area, leaf area index (LAI), and size diversity are highly intercorrelated and act in combination to affect the overall reflectance, or brightness, of a forest stand. Overstory live density and understory total living cover relate strongly to stand greenness, particularly TM band 4. 相似文献
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Sparse CCA using a Lasso with positivity constraints 总被引:1,自引:0,他引:1
Canonical correlation analysis (CCA) describes the relationship between two sets of variables by finding linear combinations of the variables with maximal correlation. A sparse version of CCA is proposed that reduces the chance of including unimportant variables in the canonical variates and thus improves their interpretation. A version of the Lasso algorithm incorporating positivity constraints is implemented in tandem with alternating least squares (ALS), to obtain sparse canonical variates. The proposed method is demonstrated on simulation studies and a data set from market basket analysis. 相似文献
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Many real life decision making problems can be modeled as discrete stochastic multi-attribute decision making (MADM) problems. A novel method for discrete stochastic MADM problems is developed based on the ideal and nadir solutions as in the classical TOPSIS method. In a stochastic MADM problem, the evaluations of the alternatives with respect to the different attributes are represented by discrete stochastic variables. According to stochastic dominance rules, the probability distributions of the ideal and nadir variates, both are discrete stochastic variables, are defined and determined for a set of discrete stochastic variables. A metric is proposed to measure the distance between two discrete stochastic variables. The ideal solution is a vector of ideal variates and the nadir solution is a vector of nadir variates for the multiple attributes. As in the classical TOPSIS method, the relative closeness of an alternative is determined by its distances from the ideal and nadir solutions. The rankings of the alternatives are determined using the relative closeness. Examples are presented to illustrate the effectiveness of the proposed method. Through the examples, several significant advantages of the proposed method over some existing methods are discussed. 相似文献
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《国际计算机数学杂志》2012,89(12):2441-2460
ABSTRACTWe propose a new variance reduction method for the price and the sensitivities of basket options under time-changed Brownian motion models. The new algorithm combines the pathwise derivative method with control variates, conditional Monte Carlo, and randomized quasi–Monte Carlo. Control variates are constructed by using the conditioning variables of lower bounds of basket options for the purpose of variance reduction. Conditional Monte Carlo further reduces the variance by integrating out the selected conditioning variable. The smoothing effect of conditional Monte Carlo enhances the pathwise derivative and the randomized quasi–Monte Carlo methods. Computational experiments show that the new algorithm yields significant variance reductions. 相似文献
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A number of approaches have been proposed for constructing alternatives to principal components that are more easily interpretable, while still explaining considerable part of the data variability. One such approach is employed in order to produce interpretable canonical variates and explore their discrimination behavior, which is more complicated as orthogonality with respect to the within-groups sums-of-squares matrix is involved. The proposed simple and interpretable canonical variates are an optimal choice between good and sparse approximation to the original ones, rather than identifying the variables that dominate the discrimination. The numerical algorithms require low computational cost, and are illustrated on the Fisher’s iris data and on moderately large real data. 相似文献
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M. C. CHENG 《International journal of control》2013,86(3):223-240
For a stationary Gaussian input, the output autocorrelation functions of the full-wave smooth and hard limiters are derived by applying Blaehman's (1964) technique. A new method is used, in conjunction with Plackett's (1954) reduction formula for the case of four Gaussian variates, to evaluate the probability that these variates are positive, when their covariance matrix is of a specific form. On combining this method with Blaehman's technique, the autocorrelation functions of the half-wave smooth and hard limiters are derived in terms of the dilogarithm function and its real part, both of which have been studied and tabulated. 相似文献
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Lining Yu 《Computational statistics & data analysis》2006,51(3):1822-1839
S-distributions are univariate statistical distributions with four parameters. They have a simple mathematical structure yet provide excellent approximations for many traditional distributions and also contain a multitude of distributional shapes without a traditional analog. S-distributions furthermore have a number of beneficial features, for instance, in terms of data classification and scaling properties. They provide an appealing compromise between generality in data representation and logistic simplicity and have been applied in a variety of fields from applied biostatistics to survival analysis and risk assessment. Given their advantages in the single- variable case, it is desirable to extend S-distributions to several variates. This article proposes such an extension. It focuses on bivariate distributions whose marginals are S-distributions, but it is clear how more than two variates are to be addressed. The construction of bivariate S- distributions utilizes copulas, which have been developed quite rapidly in recent years. It is demonstrated here how one may generate such copulas and employ them to construct and analyze bivariate—and, by extension, multivariate—S-distributions. Particular emphasis is placed on Archimedean copulas, because they are easy to implement, yet quite flexible in fitting a variety of distributional shapes. It is illustrated that the bivariate S-distributions thus constructed have considerable flexibility. They cover a variety of marginals and a wide range of dependences between the variates and facilitate the formulation of relationships between measures of dependence and model parameters. Several examples of marginals and copulas illustrate the flexibility of bivariate S-distributions. 相似文献