结果发现随机行走价格样本函数中阶段性趋势普遍存在，移动平均线法对每一模拟样本函数均能获得高额回报。
The results are: it is very common that there are trends in Monte Carlo random walk we can get excessive return with moving average method.
本文首次引入了模糊样本函数的连续性的基本概念，并且成功地解决了模糊随机过程的连续性这一最基本的理论问题。
In this paper we introduce the concept of continuity of a fuzzy sample function, and solve the basic theoretical problem of continuity of fuzzy stochastic processes successfully.
由于PBD算法需要在估计样本函数的同时估计PSF的参数，一般采用的PSF的模型较为复杂，计算量大，收敛慢；
The PBD algorithm needed to simultaneously estimate the specimen function and the parameters of the PSF, while the PSF model was complicated, needed a large number of computation and converged slowly.
清单2包含了一个简化的清单；您可以通过下载样本代码来访问所有函数(请参阅参考资料)。
Listing 2 contains a simplified listing; you can access the full functions by downloading the sample code (see Resources).
因此，要使用用户名db2inst1和密码ibmdb2连接到样本数据库SAMPLE ，可以在我们的脚本中使用下面这个函数
So, to connect to our sample database SAMPLE with user name db2inst1 and password ibmdb2, we can use the following function in our scripts
下面几部分再看一下在样本中是如何对这些函数进行编码的，同时还将讨论关系(relationship)、模板(template)和范围(scope)等概念。
The following sections review these functions as they are coded in the sample, and discuss the concepts of relationship, template and scope.
清单5：client . c；使用库中printstring函数的样本客户。
Listing 5: Client.c; a sample client that USES the function printstring from the library.
您可以利用此函数将您的样本方差计算转换为样本标准差计算，如清单8所示。
You can use it to turn your variance measurement into a standard deviation measurement, as shown in Listing 8.
提出了基于损失函数的模糊判决支持向量机算法，并与模糊样本支持向量机算法进行了比较。
The fuzzy judgment support vector machine algorithm based on loss function is proposed, and compared with fuzzy sample support vector machine algorithm.
证明了样本自相关函数的强收敛性，并得到了强收敛速度。
The strong convergence of sample autocorrelation is proved, and the convergence rate is obtained.
前向网络利用反向传播算法训练多层网络，使训练后的网络较好地拟合样本空间中各点的函数值。
Feedforward networks use back propagation algorithm to train a multi-layer network. After training, the multi-layer network can fit the function in the data space very well.
回归软件程序将样本点用图表示，并且给出最拟合样本点的直线的价值函数公式。
Regression software programs graph the sample points and give the (cost-function) formula of the straight line that best fits the points.
利用误差样本平均归一化自相关函数，可以对所抽取的数据独立性进行验证，同时为改进数据处理方法提供依据。
To validate the independency of data, we can using the average autocorrelation function of error swatch, it can also offer thereunder to ameliorate the method of disposing data.
本文的目的在于，对于线性平稳时间序列的样本、自协方差、自相关和偏相关函数的渐近性质，给出一个比较系统的描述。
The aim of this paper is to give a systematic account of asymptotic properties of the sample autocovariance, autocorrelation and partial autocorrelation functions of linear stationary time series.
在此基础上提出了基于样本评价组合有效性的综合模型，并根据生产函数理论讨论了这一模型的意义。
Then the author offers a synthetical model based on sample evaluation combination efficiency and discusses the meaning of this model according to produce function theory.
但计算很是困难，原因就是删失数据后样本的似然函数的形式很复杂。
But it was difficult to calculate, because censored data in the form of samples of the likelihood function is very complex.
首先选取产品样本，然后采用函数逼近方法建立评价模型。
It is to select product samples, then to use methods of function approximation and set up model.
引入生产要素的势效系数，将结构生产函数模型转化为对任意一组样本成立的恒等式。
We Introduce produce elements factor of tendency result coefficient, turn structure production function model into identity that come into existence to every group of sample.
首先，利用“类属函数”对原始样本进行预处理，以提高聚类样本的质量；
At first, the original samples were pretreated by using the membership class function that can improve the quality of cluster sample.
新的目标准则函数考虑了数据集样本的模糊隶属关系和样本几何分布两个方面的因素，使算法的鲁棒性和分类的正确性大大加强。
Two factors are considered in cluster-validity criterion to enhance the robustness of algorithm and the validity of clustering, one is fuzzy membership and the other is geometric property.
本文讨论了如何利用专家经验和样本训练得到启发评价函数，并得到一种修正算法。
This paper discusses how to make use of experts', experience and specimen train to get the inspired evaluating functions, and a revised algorithm of the heuristic scouting algorithm has been obtained.
此数目包括执行此函数所调用的各个函数时收集的样本。
The number includes samples that were collected when functions that were called by this function were executing.
此数目包括函数所调用的各个函数中收集的样本。
This number includes samples that were collected in functions that were called by the function.
按函数组织分析数据，并列出收集样本时正在执行的函数。
Organizes profiling by function, and lists the functions that were executing when samples were collected.
这个功能作为递归应用程序工作良好，如清单4中的样本阶乘函数所示。
This functionality works well as a recursive application, as shown in the sample factorial function in Listing 4.
按模块组织分析数据，并列出收集样本时正在执行的函数、源代码行和指令。
Organizes profiling data by module, and lists the functions, source code lines, and instructions that were executing when samples were collected.
大量独占样本表示函数自身使用了大量时间。
A large number of exclusive samples indicates that significant time was spent in the function itself.
支持向量机方法能够解决小样本情况下非线性函数拟合的通用性和推广性的问题，是求复杂的非线性拟合函数的一种非常有效的技术。
The problems of universality and extensibility in nonlinear function approximation using small samples can be solved by the method, it a very efficient technique for nonlinear function approximation.
其次，提出了基于属性效用函数估计的学习样本构造方法，从决策问题本身抽取学习样本。
Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented.
其次，提出了基于属性效用函数估计的学习样本构造方法，从决策问题本身抽取学习样本。
Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented.
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