基于BP网络的保护渣性能预测模型的研究

结合BP网络计算机实验研究 ,建立了保护渣化学组成与性能的预测模型 ,并利用化学组成与性能的关系 ,对网络的实用性进行了检验 ,结果符合保护渣化学组成与性能的关系。保护渣粘度随着保护渣碱度的增大而减小 ,而半球点温度随碱度增大而增大 ;保护渣的半球点温度和粘度都随着渣中CaF2 含量的增加而减小。用BP网络的误差反向传播算法建立的保护渣的化学组成与性能的预测模型 ,得出的预测值与实际值的误差小 ,对保护渣的设计与应用都有一定的指导作用。     

评判燃煤锅炉结渣特性的两种模式识别方法的对比

将直接模糊模式识别方法和基于Vague集的距离意义下相似度量理论引入燃煤锅炉结渣评判中.评判结果表明,二模型能较好的解决了误判断或因识别结果无显著差异而无法作出明确评判的问题.与通常模式识别方法相比,此方法所得数据结果能够使现场运行人员比较容易地得出当前运行锅炉的结渣状况.     

神经网络迭代算法在风机振动报警值中的应用

提取旋转机械D350风机转子在一段时间里的水平方向、垂直方向和轴向的振动值,以此为样本值,应用BP神经网络迭代算法进行训练,然后对D350风机的振动报警值进行预测,预测结果表明与实际报警值非常接近。     

The weak aggregating algorithm and weak mixability

This paper resolves the problem of predicting as well as the best expert up to an additive term of the order o(n), where n is the length of a sequence of letters from a finite alphabet. We call the games that permit this weakly mixable and give a geometrical characterisation of the class of weakly mixable games. Weak mixability turns out to be equivalent to convexity of the finite part of the set of superpredictions. For bounded games we introduce the Weak Aggregating Algorithm that allows us to obtain additive terms of the form .     

一种带有路由反射的BGP路由预测算法

由于路由反射器的影响,路由器可能只知道非常有限的到达外部目的地的路由的子集,BGP路径选择过程并不会形成一个确定的路径等级顺序,使得路径预测变得非常困难.给出了一种算法,该算法不需要对路由协议动态进行复杂的模拟,只需要静态的路由数据就可以计算出带有路由反射的路由选择结果,并在一个模拟的大型ISP上验证了预测算法的正确性.结果显示配置的改变可以引起路由表的变化.     

软件产品评价技术研究

文章给出对软件产品进行评价的一般过程,并介绍了对评价过程的关键环节——选择度量,建立度量评分等级,建立评估标准所进行的研究,提出了质量预测模型技术。     

人工神经网络模型在瓦斯预测中的应用

通过对神经网络的基本原理和学习算法的分析，建立了基于瓦斯涌出量预测的BP网络应用模型，采用Delphi编程语言，预测了瓦斯的涌出量，并结合实例进行了验证。     

我国近期的经济波动和宏观控制策略

确定潜在GNP是研究宏观经济波动及其控制策略的一个基本问题,但我国现有的专著和论文未见提及,国外对其确定方法也没有取得一致的意见。本文从控制工程和宏观经济理论的综合角度,对这一问题进行了以下方面的探讨,得到了一些富有意义的结果。首先,对潜在GNP提出了新的解释和新的估计方法;其次,用这种方法确定出我国近期的潜在GNP,从而划出我国1978—1989年间经济波动的幅度和周期;然后以高经济增长率和低通货膨胀率为政策目标,提出我国1990—2000年的宏观控制策略—GNP的最优轨迹,并对该轨迹下的目标均值进行了初步估计。     

Box—Jenkins预测系统

本文介绍了Ｂｏｘ－Ｊｅｎｋｉｎｓ预测系统的数学模型、预测步骤及系统特点。     

Predicting students' final performance from participation in on-line discussion forums

On-line discussion forums constitute communities of people learning from each other, which not only inform the students about their peers' doubts and problems but can also inform instructors about their students' knowledge of the course contents. In fact, nowadays there is increasing interest in the use of discussion forums as an indicator of student performance. In this respect, this paper proposes the use of different data mining approaches for improving prediction of students' final performance starting from participation indicators in both quantitative, qualitative and social network forums. Our objective is to determine how the selection of instances and attributes, the use of different classification algorithms and the date when data is gathered affect the accuracy and comprehensibility of the prediction. A new Moodle's module for gathering forum indicators was developed and different executions were carried out using real data from 114 university students during a first-year course in computer science. A representative set of traditional classification algorithms have been used and compared versus classification via clustering algorithms for predicting whether students will pass or fail the course on the basis of data about their forum usage. The results obtained indicate the suitability of performing both a final prediction at the end of the course and an early prediction before the end of the course; of applying clustering plus class association rules mining instead of traditional classification for obtaining highly interpretable student performance models; and of using a subset of attributes instead of all available attributes, and not all forum messages but only students' messages with content related to the subject of the course for improving classification accuracy.