沪宁铁路沿线城市产业转移预测模型构建及应用问题研究

发布时间：2018-02-25 04:29

本文关键词： 灰色定权聚类方法马尔可夫预测模型区间GERT网络白化权函数概率矩阵　出处：《南京航空航天大学》2013年硕士论文　论文类型：学位论文

【摘要】：稳定系统受冲击作用的影响，其结构和变动方向将会发生变化。本文拟解决复杂大系统受外界冲击作用，带来的系统稳定性变动等变化，以系统的构成元素为研究对象，通过将系统的各组成对象划分不同的子系统，了解系统内部构造，针对不同子系统组成对象的特征分别构建预测模型，预测系统的演化与发展方向。本文共分为六章，通过层次划分、模型构建到结果预测，逐步深入了解系统结构与发展趋势。第二章构建指标体系将复杂的系统结构条理化，完成对复杂系统的初步了解，实现将系统内不同的指标划分为不同层次：区域性中心对象以及非区域性中心对象。第三、四章构建灰色马尔可夫预测模型，对区域性中心对象演化方向进行预测。第五章构建区间GERT网络模型，对非区域性中心对象之间发生转移的相关参数进行预测。本文共构建三种模型：层次结构划分模型、马尔可夫预测模型以及区间GERT网络模型。在广大学者研究的基础上，，本文作者对上述三种模型的进行局部改进：（1）层次结构划分模型。对灰色白化权函数转折点的选取方法进行改进，得到改进转折点的灰色定权聚类分析方法，筛选能够体现系统对象的地位和功能指标，建立指标体系，构建改进转折点的灰色定权聚类分析方法，划分系统层次，验证改进后的白化权函数性质，设计模型的求解方法，实现对系统层次结构的划分。（2）马尔可夫预测模型。对传统的马尔可夫预测模型的概率转移矩阵进行改进，将概率矩阵从实数矩阵扩展到区间概率矩阵。通过对原始序列与预测序列的误差值，确定不同状态的区间范围，将不同范围内的误差值划分为不同的状态，得到指标的区间概率矩阵，归一化概率矩阵得到改进后的灰色马尔可夫模型。（3）区间GERT网络模型。传统的GERT网络的信息流参数均为实数，本文改进GERT网络信息流参数范围，构建网络节点不同时间节点下的参数序列，考虑节点自身发生转移以及节点间发生转移的信息流变化情况，得到各参数信息流的区间序列，完成对区间GERT网络模型构建。考虑沪宁高速铁路线的开通对沿线城市经济发展的影响，通过第二章构建的层次结构模型划分城市群的层次，将沪宁高速铁路沿线城市群划分为区域性中心城市和非区域性中心城市，第三、四章构建的灰色马尔可夫预测模型，预测了沪宁高速铁路沿线区域性中心城市未来发展方向，第五章构建的区间GERT网络模型，预测了非区域性中心城市产业转移的信息流参数。
[Abstract]:Under the influence of shock, the structure and direction of the stability system will change. This paper aims to solve the changes of the stability of the complex large scale system by the external shock, and take the component elements of the system as the research object. By dividing each component object of the system into different subsystems, the internal structure of the system is understood, and the prediction model is constructed according to the characteristics of the different subsystem components to predict the evolution and development direction of the system. This paper is divided into six chapters. By dividing the level, constructing the model to forecast the result, we can understand the system structure and the development trend step by step. In the second chapter, the index system will be constructed and the complex system structure will be organized, and the preliminary understanding of the complex system will be completed. The realization of the system will be divided into different indicators: regional central object and non-regional central object. In Chapter 5th, an interval GERT network model is constructed to predict the parameters related to the transition between non-regional central objects. In this paper, three kinds of models are constructed: hierarchical structure partition model, Markov prediction model and interval GERT network model. The method of selecting turning point of grey whitening weight function is improved, and the grey weight clustering analysis method of improved turning point is obtained, which can reflect the status and function index of system object, and establish the index system. The grey weight clustering analysis method of the improved turning point is constructed, the system hierarchy is divided, the properties of the improved whitening weight function are verified, the solution method of the design model is designed, and the division of the system hierarchy is realized. 2) Markov prediction model. The probability transfer matrix of the traditional Markov prediction model is improved, and the probability matrix is extended from the real number matrix to the interval probability matrix. The interval range of different states is determined and the error values in different ranges are divided into different states. The interval probability matrix of the index is obtained and the improved grey Markov model is obtained by normalizing the probability matrix. The information flow parameters of traditional GERT networks are all real numbers. This paper improves the range of information flow parameters of GERT networks and constructs the parameter sequences of network nodes under different time nodes. Considering the change of information flow between nodes and between nodes, the interval sequence of each parameter information flow is obtained, and the model of interval GERT network is constructed. Considering the impact of the opening of the Shanghai-Nanjing high-speed railway line on the economic development of the cities along the line, the hierarchical structure model constructed in the second chapter is used to divide the urban agglomeration. The urban agglomeration along the Shanghai-Nanjing high-speed railway is divided into regional central cities and non-regional central cities. In the third and fourth chapters, the grey Markov forecasting model is constructed to predict the future development direction of the regional central cities along the Shanghai-Nanjing high-speed railway. In chapter 5th, the interval GERT network model is constructed to predict the information flow parameters of industrial transfer in non-regional central cities.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：O211.62;F127

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