铁路行包运量预测模型研究

铁路行包运量预测是以运输需求和内部供给为导向,综合考虑各种影响因素,对行包运量现状和发展的正确把握.探讨利用人工神经网络结合主成分分析的方法,建立铁路行包运量预测模型,解释并预测行包专列开行后铁路行包运量的增长趋势.实例分析的仿真结果表明,采用主成分分析法的广义回归神经网络模型结构简洁、预测精度高、收敛速度快,对相关铁路部门和企业的决策具有参考意义.     

Communication channels,innovation tasks and NPD project outcomes in innovation-driven horizontal networks

Inter-organizational collaborations and horizontal networks are increasingly playing a pivotal role in innovations and new product development among firms. In this study, we investigate the link between the innovation task analyzability and the richness of communications channels used in network arrangements and the link between task analyzability and ties and project development time. We investigated the links based on the data collected from 372 respondents representing ninety three different innovation-driven horizontal networks. The results of structural equation modeling reveal a negative link from task analyzability to communication channel richness and a positive link from communication channel richness to ties. Communication channel richness was found to partially mediate the influence that task analyzability has on both NPD project outcomes of ties and development time. The implications of our results for theory and practice are discussed.     

气体检测系统中BP神经网络的设计与实现

在简要概述气体检测系统原理的基础上,分析了BP神经网络模式识别的特性和结构,阐述了一种基于人工神经网络的混合气体检测方法。分析并研究了气体检测系统中BP神经网络结构的设计方法,构建了基于Matlab的BP网络模型,并实现了对CO、H2S和CH4三种混合气体的定性定量检测。     

基于技术体制视角的高科技产业集群演变——以台湾中小企业为例

在对产业集群的分类和演变模式进行总结的基础之上,从技术体制视角,对台湾中小企业的案例进行了分析。研究证实:在全球化高科技产业竞争中,台湾产业集群竞争优势获得的关键要素是——内部和外部知识链的协同演进。按照传统模式运营的企业,需要通过产业集群内的企业互联和加入全球生产网络的方式,并融入更广阔的全球化渠道以重组知识链。     

基于E-BP神经网络的科技奖励评价模型研究

根据简单多数原则引入专家动态权数,与人工神经网络BP算法相结合,构建E-BP科技奖励综合评价智能模型。实证分析表明,该模型减少了传统科技奖励评价方法中受专家主观因素和模糊随机因素的影响,使评价结果更加客观、合理。     

集群内创业网络对创业资源的影响研究

根据Timmons创业过程模型,成功的创业活动需要将创业机会、创业团队和创业资源三者作适当搭配,在取得必要的资源和组成创业团队之后,才开始创业过程。新创企业的不确定性或弱势使其难以得到外部的支持,会面临突出的资源约束问题。集群内创业网络的构建,有助于新创企业在网络中获取创业资源,提高创业绩效。研究界定了集群内创业网络及其维度、创业资源及其类型,具体分析了集群内创业网络不同维度对创业资源获取的影响,建立了集群内创业网络对获取创业资源的影响模型。     

中国融入东亚生产网络的贸易利益——基于20个工业部门净附加值的分析

本文结合中国融入东亚生产网络的现状,采用反映进口中间产品在国内的产业循环效应或产业波及效果的净附加值指标来衡量中国融入东亚生产网络后归属于中国的直接的贸易利益。基于1992-2007年期间中国20个工业部门从东亚进口的中间产品在国内产业循环后所创造的净附加值分析表明:从净附加值的绝对量来看,中国工业部门从东亚进口中间产品所创造的净附加值增长迅猛,且在从世界进口的中间产品所创造的总的净附加值中平均占了42%左右的份额,但该净附加值占中国工业部门总出口的比重总体上不高且年均增长幅度有限。细分行业的考察发现,来自东亚的中间产品创造的净附加值比重较高的部门主要是技术、资本密集型工业部门,但在大多数工业部门中这一比重增长不显著。因此,应在进一步深化中国与东亚的垂直专业化分工的基础上,通过增强本土企业的技术吸收能力和自主创新能力来提升本土企业在东亚生产网络中的分工地位,以获取更多的贸易利益。     

Dynamical clustering of exchange rates

We use techniques from network science to study correlations in the foreign exchange (FX) market during the period 1991–2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clustering of the exchange-rate time series, we investigate dynamic communities in the network. We show that there is a relationship between an exchange rate's functional role within the market and its position within its community and use a node-centric community analysis to track the temporal dynamics of such roles. This reveals which exchange rates dominate the market at particular times and also identifies exchange rates that experienced significant changes in market role. We also use the community dynamics to uncover major structural changes that occurred in the FX market. Our techniques are general and will be similarly useful for investigating correlations in other markets.     

Tourist market segmentation with linear and non-linear techniques

The need for in-depth knowledge of tourist market segments and the need to overcome the limitations of using linear techniques to analyse non-linear relationships requires a re-assessment of generally used approaches such as cluster analysis and multiple linear regression. The objectives of the research are (1) to consider the use of self-organising (SOM) neural networks for segmenting tourist markets and (2) to analyse the predictive ability of backpropagation (BP) neural networks for classifying tourists from follow-up surveys by using the output provided by a SOM neural network. The findings of the SOM neural network modelling indicate three natural clusters. In addition, the predictive ability of the BP neural network model appears to be superior to that of MLR static filter and logistic regression models. The BP neural network model developed for this application appears suitable for deployment (i.e. classification of tourists from follow-up surveys).     

Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.