很多同学都是第一次参加数学建模比赛,对于数据分析小白来讲,如何才能在数学建模比赛中通过短期学习冲刺奖项?其实只需要掌握一些常用模型典型算法,并且会用软件快速完成即可。数学建模常用模型大概可以分为以下四大类:分别是评价模型、预测模型、优化模型、数理统计模型。接下来将进行详细说明。
评价模型用于对某个系统、方案或决策进行评估。通过构建合适的指标和评价方法,评价模型能够对不同方案的优劣进行比较和分析。在数学建模比赛中,评价模型通常根据问题的特点和需求,设计合适的评价标准和指标,对不同方案或模型的性能进行评估和比较,以帮助做出决策。
预测模型能够根据过去的数据和观察结果,对未来的趋势、行为或结果进行预测和推断。预测模型常用于分析时间序列数据、趋势预测、行为模式预测等问题。在数学建模比赛中,预测模型可以根据给定的数据集或者特定规律,构建合适的数学模型,进行未来趋势预测,从而帮助做出决策或规划。
优化模型旨在找到使某个目标函数取得最大或最小值的最优解。优化模型适用于求解最佳决策、资源分配、排产安排等问题。在数学建模比赛中,优化模型可以通过建立数学规划模型,确定决策变量、约束条件和目标函数,利用求解方法寻找最优解或次优解,以优化问题的方案或决策。
- 数理统计模型(Statistical Model)
数理统计模型用于对数据进行分析、总结和推断。它能够通过建立概率模型和统计分布,对数据的特征、关系和不确定性进行描述和推断。在数学建模比赛中,数理统计模型可以通过对给定数据集的统计分析,推断出数据的分布规律、相关性、假设检验等,为问题提供支持和解决方案。
评价类、预测类、数理统计类模型常用算法如下:
接下来,将逐个模型进行说明。
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)
AHP层次分析法是一种解决多目标复杂问题的定性和定量相结合进行计算决策权重的研究方法。它通过构建层次结构,将复杂的决策问题分解成多个层次,并使用专家判断和比较来确定各个因素的权重,从而得出最终决策结果。比较有效地应用于那些难以用定量方法解决的课题。
AHP层次分析法包括两个步骤,分别是权重计算和一致性检验(SPSSAU会默认输出);
判断矩阵解读:门票相对于景色来讲,重要性更高,所以为3分;相反,景色相对于门票来讲,则为0.33333分。交通相对于景色来更重要为2分,其余类似下去。
TOPSIS法是一种基于距离和相似性度量的多属性决策方法。TOPSIS法首先将多个备选方案与理想解进行比较,计算每个备选方案与理想解之间的相似性和距离。然后根据计算结果,评估和排序各个备选方案,选择最佳的方案。TOPSIS法能够较好地处理多属性决策问题,特别适用于需要考虑多个评价指标的情况。
第一步:准备好数据,并且进行同趋势化处理(需要研究者自行处理);
第二步:数据归一化处理解决量纲问题(数据处理->生成变量,通常选择‘平方和归一化’);
第三步:找出最优和最劣矩阵向量(SPSSAU自动处理);
第四步:分别计算评价对象与正理想解距离D+或负理想解距离D-;
第五步:结合距离值计算得出接近程序C值,并且进行排序,得出结论。
模糊综合评价是一种处理具有模糊信息的评价方法。在模糊综合评价中,将模糊的评价指标通过隶属度函数转化为隶属度,然后根据权重给予不同指标不同的重要性。最后,通过对隶属度进行加权求和,得到一个综合评价结果。模糊综合评价方法能够有效处理不确定性和模糊性的问题,适用于现实世界中的复杂决策。
灰色关联分析法通过研究数据关联性大小(母序列与特征序列之间的关联程度),通过关联度(即关联性大小)进行度量数据之间的关联程度,从而辅助决策的一种研究方法。
第二步:针对数据进行无量纲化处理(通常情况下需要);
第三步:求解母序列和特征序列之间的灰色关联系数值;
灰色关联法详细说明及案例操作解读请点击查看下方帮助手册
数据包络分析DEA是一种多指标投入和产出评价的研究方法。其应用数学规划模型计算比较决策单元(DMU)之间的相对效率,对评价对象做出评价。
数据包络分析DEA时,首先需要分析综合效益值θ,即首先判断DMU是否有DEA有效,如果有效,则说明该DMU较优,反之说明‘非DEA有效’,即相对来说还有提升空间,那么提升空间具体在哪里呢,比如提高还是减少规模呢,可以通过规模效益分析得到。与此同时,如果是‘非DEA有效’,那么具体问题是什么,投入冗余还是产出不足,则可以通过对应的投入冗余 或产出不足分析表格得出,具体数字直接查看松驰变量即可。
秩和比(RSR)方法是一种基于排序的模型比较方法。其实质原理是利用了RSR值信息进行各项数学计算,RSR值介于0~1之间且连续,通常情况下,该值越大说明评价越‘优’。
第一步:列出原始数据,一行代表一个评价对象,一列代表一个评价指标。最终为m*n矩阵;
第三步:利用Step2的秩值,计算得到RSR值和RSR值排名;
第四步:列出RSR的分布表格情况并且得到Probit值;
数学建模预测类模型通常用来根据现有数据和已知信息,进行未来事件、趋势或结果的预测和预估。这些模型通过分析历史数据、寻找规律和趋势,并由此推断未来的发展趋势和可能的结果。可以帮助人们做出合理判断、做出准确决策提供参考依据。
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是一种将不同的预测模型或方法结合起来,以提高预测准确度和稳定性的方法。优势在于能够利用不同模型的长处,减少个别模型的局限性和不确定性带来的影响,提高预测的准确性和稳定性。常见的组合预测法有平均法、加权平均法、逐步加权法、组合模型法等。
聚类分析方法可分为Q型和R型,如下图:
![](data:image/png;base64,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)
主成分分析(PCA)是一种常用的数据降维和特征提取方法,它可以将具有相关性的多个变量转换为少数几个无关的主成分。这样可以简化复杂的数据结构,减少冗余信息,提高模型预测精度和解释能力,在数学建模中具有广泛的应用。
因子分析和主成分分析都是常用的数据降维方法,但它们的应用场景略有不同。主成分分析适合于变量之间相关性较高、需要提取重要变量或简化数据结构的情况;而因子分析适合于探索潜在因素、发现变量之间的共性或隐含关系的情况。选择哪种方法取决于具体的分析目的和数据特征。
判别分析(distinguish analysis)是一种机器学习算法,其用于在分类确定前提下,根据数据的特征研究数据归类问题。比如结合消费者的特征数据(比如消费金额、消费频次、购物时长、购买产品种类等),以预测消费者属于某种类型的顾客(款式偏好型、质量在乎型、价格敏感型等)。
相关分析用于分析变量之间的相关关系,包括是否有关系以及关系的紧密程度。在数学建模中,相关分析可用于衡量变量的关联性、选择变量、验证假设和推断、变量优化等。SPSSAU提供三类相关系数:Pearson系数、Spearman系数、Kendall协调系数。
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)
第一步:首先判断是否有关系(有*号则表示有关系,否则表示无关系);
第二步:判断关系为正相关或者负相关(相关系数大于0为正相关,反之为负相关);
第三步:判断关系紧密程度(通常相关系数大于0.4则表示关系紧密)。
回归分析简单来讲就是用于分析自变量X与因变量Y之间的影响关系的方法。回归分析主要基于自变量X的值预测因变量Y的值,通过构造回归模型,帮助理解自变量如何影响因变量,以及各个自变量对因变量的影响程度。
回归分析可细分为20多种,常用的有多元线性回归、logistic回归、岭回归、逐步回归、lasso回归等。
数学建模中常用的假设检验模型包括t检验、方差分析、卡方检验、非参数检验等,它们可以用于判断样本和总体之间是否存在显著差异,以及变量之间是否存在相关性或影响关系。这些模型可以帮助我们验证研究假设,评估数据的可靠性和有效性,并帮助我们作出科学严谨的结论和决策。