兰州大学机构库 >数学与统计学院
基于统计分析与数据挖掘的智能优化预测研究及应用
Alternative TitleResearch and application of intelligent optimization forecast based on statistical analysis and data mining
董瑶
Thesis Advisor王建州
2015-05-23
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name博士
Keyword数据分析 人工智能优化算法 统计分布 径向基神经网络 惩罚函数
Abstract本研究首先证明三种人工智能优化算法的收敛性,然后提出了三类预测模型:(1)利用三种人工智能优化方法对三种灰色模型的参数进行优化估计,通过构造不同的目标函数,提出了基于人工智能参数估计方法的单变量灰色模型;(2)双参数的Weibull分布、Lognormal分布和Gamma分布能够分析单个随机变量取值的概率规律,为了使概率分布函数与数据的真实分布之间的拟合度达到最好,本研究提出了基于人工智能优化参数估计的方法,它们通过最小化构造的损失函数来建立单变量统计分布模型;(3)径向基(RBF)神经网络在建模时不仅需要较多的隐含层节点数目而且预测精度也不高,本研究将LASSO和Hard-ridge选择变量的特性引入RBF神经网络参数线性化的结构中,通过对隐含层节点的选择来实现模型简化的目的。
Other AbstractThis paper first proves the convergence of three artificial intelligent optimization algorithms, then proposes three kinds of forecasting models: (1) This paper uses three artificial intelligent optimization algorithms to estimate the parameters of three grey models. Through establishing different objective functions, univariate grey models based on artificial intelligent parameter estimation methods are proposed; (2) Weibull, Lognormal and Gamma distribution can analyze the probability of a single random variable, in order to get the best fitting degree between probability density function and the actual data distribution, the methods based on artificial intelligent parameter estimation that minimizes the loss functions are proposed to build univariate statistical distribution models; (3) Radial basis (RBF) neural network not only needs a lot of number of nodes in hidden layer but also has low forecasting accuracy, this paper puts the characteristics of variables selection of LASSO and Hard-ridge into the linear-in-the-parameters RBF neural network, and achieves the purpose of simplifying models by selecting the nodes in hidden layers.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225110
Collection数学与统计学院
Recommended Citation
GB/T 7714
董瑶. 基于统计分析与数据挖掘的智能优化预测研究及应用[D]. 兰州. 兰州大学,2015.
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