关键词： Grey correlation   fuzzy mathematics   price appraisal   intelligent computing  

摘要： The precision of price appraisal is directly linked to the sound development of a market-oriented economy and public benefit. Market comparison approach is a widely used price-appraisal algorithm. However, when the sample size is small, the computing precision of the market comparison approach is severely impaired. To realize precise, intelligent computing under the conditions of a small sample size and a low degree of proximity between samples and the object to be evaluated, this study adopted grey correlation and fuzzy mathematics to optimize the market comparison approach for price appraisal. The proposed algorithm used grey correlation analysis to quantify the weight of various attributes influencing price appraisal. The price correlation between samples and the object to be evaluated was confirmed. The fuzzy mathematical method was then used to obtain the relative weight of samples. The proposed intelligent price-appraisal algorithm was constructed to improve the market comparison approach. Finally, several residences in Hangzhou, Zhejiang province, China were chosen for an empirical analysis and for the verification of the feasibility of the improved price-appraisal algorithm. Results suggest that (1) The mean error of the algorithm is only 0.99%, indicating high computing precision;(2) When the number of objects to be evaluated is large, a computer system can be used to aid the intelligent computing and to efficiently perform intelligent computing. In conclusion, the proposed algorithm can maintain high computing precision under the conditions of a small sample size and a low degree of proximity, and the algorithm can be used as a theoretical basis to realize intelligent mass appraisal. Overall, the proposed algorithm is worthy of further development because it is highly operational.

关键词： 数学建模   核心算法   计算思维  

摘要： 信息与计算科学,由于其自身的特征,对我们的思维能力提出了较高要求,所以我们理解、学习的难度较大。基于此,本文将以数学计算、非数学计算为切入点,并以例题为基础分析培养计算思维能力的方式,旨在采取科学的学习方法,培养学习兴趣,降低本学科的学习难度,提高信息与计算科学的学习效果。

关键词： 贝叶斯网络   参数学习   单调性提取   单调性约束  

摘要： 随着人工智能技术受到人们越来越广泛的重视和应用,贝叶斯网络作为经典的机器学习算法,以其概率推理准确和语义表达清晰的优势,在不确定性建模和概率推理等问题上得到了广泛的应用。贝叶斯网络参数学习作为贝叶斯网络研究和应用的一个重要的问题和挑战,准确地学习网络参数对贝叶斯网络模型的准确性和可解释性具有重要的影响。在现实世界的很多应用中,人们能够收集得到的样本数据是有限的。所以,针对有限的训练样本数据,如何提升网络参数学习的准确性是一个重要的研究课题。同时,有学者研究表明单调性广泛存在于生活中的各个领域,在很多问题中属性与属性、属性与决策之间存在着一定单调性关系。本文围绕着有限训练样本数据的贝叶斯网络参数学习问题,基于贝叶斯网络节点之间的单调性关系和贝叶斯网络的单调性展开研究,主要的研究工作和创新点如下:(1)本文提出了一种纯数据驱动的单调贝叶斯网络参数学习方法。首先,该方法通过单调性衡量指标自动地提取网络节点之间的单调性关系;然后,基于贝叶斯网络的单调性定义构造网络节点条件概率表的单调性不等式约束,将贝叶斯网络参数学习转化为一个约束优化问题;最后,通过不同的方法对该问题进行求解。在公开的标准贝叶斯网络库和真实分类数据集上的实验验证了本文提出的方法在有限训练样本数据上的有效性。(2)针对现实中获得的样本通常带有缺失数据,本文提出了一种带单调性约束的EM改进算法。改进后的EM算法通过在每一轮迭代的M步求解中添加基于贝叶斯网络单调性定义构造得到参数单调性不等式约束,将最大化期望似然函数转为约束优化问题。改进后的EM算法使得在M步求解得到的网络参数满足网络节点之间单调关系,降低了EM算法对训练样本数据量的依赖。在不同缺失率数据集上的实验结果表明了改进后的EM算法提升了参数学习的准确性。

关键词： 算法初步   编写程序   高中数学   数学教师  

摘要： 随着信息技术的迅速发展,多媒体技术越来越多地融入到高中数学课堂教学中,因此在数学教学中增加"算法初步"的教学内容势在必行。算法思想是古代数学的主要特征和重要思想,也是现代人必须具备的数学素质。数学教师有必要对其进行研究、探讨,以采取恰当的教学策略,提高教学效率。一、找准教学切入点所谓教学的切入点,就是"算法初步"教学的突破口。算法教学既不同于传统的数学教学过程,也不同

关键词： Mathematical Model   Simulation results   Welding   trajectory control   trajectory   Interpolation Arithmetic   welding torches  

摘要： This paper presents welding torch pose model and interpolation algorithm of trajectory control of saddle-shaped weld formed by intersection of two pipes;the working principle, interpolation algorithm, welding experiment, and simulation result of the automatic welding system of the saddle-shaped weld are described. A variable angle interpolation method is used to control the trajectory and pose of the welding torch, which guarantees the constant linear terminal velocity. The mathematical model of the trajectory and pose of welding torch are established. Simulation and experiment have been carried out to verify the effectiveness of the proposed algorithm and mathematical model. The results demonstrate that the interpolation algorithm is well within the interpolation requirements of the saddle-shaped weld and ideal feed rate stability.

关键词： 复杂性疾病   miRNAs   生物信息   数学理论   核矩阵分解  

摘要： 二十一世纪以来，高通量技术和生物医学的高速发展催生出大规模生物数据，为研究学者从系统的角度分析疾病发生机制、精确诊断疾病、制定治疗方案以及设计药物等方面提供了强大的数据基础。本文旨在结合运筹学和生物信息学的研究方法，利用高通量组学数据对疾病和药物相关领域的重要问题进行探索，主要研究结果包括:　　1.复杂性疾病与miRNAs的关联预测　　基于生物医学结论“功能类似的miRNAs倾向于与表观性状类似的疾病相关，并且表观性状类似的疾病也倾向于受功能类似的miRNAs的影响”，本文提出了基于图的正则化的核矩阵分解模型(Graph Regularized Logistic Matrix Factorization，GRLMF)，从概率角度对潜在的疾病与miRNAs的关联进行预测。GRLMF不仅可以在交叉验证测试中识别出被作为测试样本的已知的与疾病相关的miRNAs，而且在重要疾病（如前列腺肿瘤、肾肿瘤、胃肿瘤）相关的潜在miRNAs预测中，其预测结果也很大程度上得到了相关数据库和文献的验证。此外，相比于先前的一些疾病-miRNA关联预测模型，GRLMF具有较好的预测精度。　　2.药物重定位预测　　基于“越相似的药物往往会治疗越相似的疾病，反之亦然”这一假设，我们从是否采用网络模型的角度，提出了两种新的药物重定位方法:(1)基于网络的模型CMIF (Cluster-based Maximizing Information Flow for Drug Repositioning),利用药物的相似性、疾病的相似性构建异质网络，从信息流角度，采用图论方法对药物与疾病之间的关联进行打分。CMIF能够将药物与疾病之间的关系可视化，根据整个网络中的信息流做预测，具有很好的全局性，在预测精度上要优于之前的一些网络预测模型。(2)非网络的模型KMF (Regularized Kernel Matrix Factorization)，利用核矩阵分解方法能够给出药物与疾病关联的概率，并且在稀疏矩阵的处理中收敛速度较快。　　3.增效药物组合预测　　本文将药物组合简化为药理特征之间的组合，提出增效药物组合预测模型SDCP (Synergistic Drug Combinations Prediction by Integrating Pharmacologic Data)。该算法用药物靶标、药物的代谢酶等药理数据构造药物组合的特征，采用分解机模型对药物之间的增效分数进行打分。实验结果表明，SDCP在两个药物的增效组合预测以及三个药物的增效组合预测中都取得了非常好的效果，并且预测出的药物组合在文献或临床试验中得到了验证。此外，算法能够对药物组合的增效原理给出生物解释，为增效机理的研究提供了新思路。

关键词： Ontology   similarity measuring   graph model   machine learning   multi-dividing setting  

摘要： As a data utility and aided tool, ontology has been widely used in many areas of the computer. Owing to its great efficiency, ontologies have also been introduced into various engineering disciplines. In this paper, we present the fundamental ideas of how to deal with similarity measuring problem in ontology learning algorithms. The mathematical basis of ontology learning algorithms is also introduced from a statistical learning theory point of view. Finally, we present two ontology learning algorithms in multi-dividing setting and ontology sparse vector learning setting, respectively.

关键词： Support vector machine   big data analytics   gaussian kernel function   teaching quality evaluation   parameter optimization  

摘要： Teaching quality evaluation is important to improve teaching quality in universities. Fuzzy comprehensive evaluation is a comprehensive method of qualitative analysis and quantitative analysis. It has been widely applied in social life, economic management and engineering technology. The scientific practice has proved that the rational distribution of the weight factors and the selection of the evaluation model directly affect the accuracy and reliability of the fuzzy comprehensive evaluation results. How to determine the weight of multiple indicators into a single index is the focus of the present study. The simulation results show that the parameters of the gaussian kernel function optimization method designed in this article has strong advantages. Also, the results show that the algorithm presented in this paper can accurately reflect the quality of teaching quality, and at the same time realize the accurate determination of the weight of teaching quality evaluation index.

关键词： 计算机编程   数学算法   优化分析  

摘要： 作为一种重要的数学思想,数学算法要求进行统一化的计算,在研究和分析各项问题的过程中,通过一种归纳总结的途径来获得最终的计算结果.近年来计算机技术的应用范围越来越广,已经成为人们工作和学习的重要组成部分,要想提升计算机软件的运用效率,就必须要从根本上提升计算机编程质量.数学和计算机学科有着密不可分的联系,二者有机整合已成为一种必然趋势.

摘要： In 2 experiments, I tested the effects of a writing/editing package for middle school students with disabilities who performed below grade level in math and reading. The participants were grouped into dyads based on math grade level, and each dyad consisted of an editor and writer. There were 8 participants in Experiment 1. The design was a multiple probe across 4 dyads design. The dependent variables were : 1) algorithm components including functional and rationale descriptions; 2) functional accuracy of a written math algorithm; 3) structural components of written responses including grammar, spelling, and punctuation; 4) social validity measure to determine whether the was a significant difference in the means of scores assigned by naïve readers for pre- and post-intervention samples; 5) resistance to extinction measures for solving above grade level algebra problems as a measure of reinforcement value. During the pre-intervention assessments, each participant was given a word problem which corresponded to their math performance level. Participants solved the word problem, then wrote an explanation so a naïve adult reader could complete the steps of the problem to produce the solution without seeing the problem. Participants also wrote responses explaining the rationale for the operations used in the word problem and the practical application for the word problem. Participants were then given untaught algebra equations to measure resistance to extinction. In Experiment 1, the independent variable was an editing and writing package for producing a written explanation for specific steps to solve a multi-step math problem. The participant as the writer produced a written math algorithm, and the participant as the editor provided questions to the writer so that a rewrite would help the editor solve the math problem correctly. Structural elements were not consequated, and rationale components were not directly taught. Criterion for the independent variable was achieve