首先，本文对一般输运网络的加权模型做了一系列的相关研究。
Firstly, we made a series researches on the weighted models of general transportation networks.
本文主要的两个核心部分如下：第一部分：建立加权复杂网络演化模型。
The two mainly core of this article are as follows: Part I: Building a weighted complex network evolution model.
特别的，我还将介绍本地搜索算法的接近查询，和一个局部聚类的节点加权方法，以及网络的网络模型集成多个网络。
In particular, I will introduce a local search algorithm for proximity query, a node weighting method for local clustering, and the network of networks model for integrating multiple networks.
通过设计基于BP神经网络的统计加权算法，建立数据融合模型。
Through design of the statistical weighting algorithm based on BP neural network, the data fusion model will be established.
第二部分：讨论了加权网络的同步能力与权重之间的对应关系，以及网络传播模型和免疫策略。
Part II: the relation between the synchronous ability of weighted network and strength is researched, also the network spread model and immunization strategy are researched too.
对结果均方差的分析显示，加权支持向量机的预测精度优于人工神经网络和标准支持向量机模型。
The analysis to the mean square deviation showed us the conclusion, that the prediction accuracy of WSVM was better than the ANN and traditional SVM models.
第三章在局部世界(LW)模型的基础上，提出了基于局部信息的从无权到加权网络的演化模型。
In chapter 3, based on local world (LW) model, the model evolves network from unweighted to weighted with local information is proposed.
第三章在局部世界(LW)模型的基础上，提出了基于局部信息的从无权到加权网络的演化模型。
In chapter 3, based on local world (LW) model, the model evolves network from unweighted to weighted with local information is proposed.
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