基于支持向量回归的现代气象雷达寿命预测

为了准确地预测气象雷达使用寿命, 提高气象雷达运行可靠性、安全性和可维护性, 提出了一种新的基于多元回归的气象雷达使用寿命预测框架。在该框架内,采用了支持向量 回归(SVR)方法来求解气象雷达使用寿命的多元回归问题;提出了基于SVR的气象雷达特征参 数选择FSSSVR（Feature Subset Selection SVR）算法去掉冗余和无效的特征参数。实验结 果表明,基于SVR方法的预测算法能够准确地预测气象雷达的使用寿命,能够为雷达全寿命 周期管理提供参考依据。     

Liberalization,Regulatory Delays and Vulnerability to Systemic Banking Crisis

This work aims at contributing to the improvement of the early warning systems of banking crises using a new approach accounting for model uncertainty. We show that a multinomial logit model based on Bayesian model averaging (BMA) is a good strategy to predict banking crisis. To do this, we argue that differences in vulnerability to banking crisis can be largely explained by an asymmetry between financial market evolution and regulation update on a sample of 49 developed and developing countries between 1980 and 2010. When markets are liberalized, competition pushes bankers to take more risks and take advantage of regulatory delays thus increasing crises probabilities. Our empirical evidence supports that crisis probability is higher in country liberalizing their banking system when regulation is not updated. We developed an early warning system for systemic banking crises based on the multinomial logit model. Its main difference to existing prediction models and its contribution to the literature is that it is intended to identify and resolve what is called by Bussiere and Fratzscher [(2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25(6), 953–973] as post-crisis bias in binomial models and to develop a new methodology of leading indicators selection based on BMA. Overall, our model predicts all banking crises during our sample period.     

基于GA-SVM模型的福建省城镇登记失业率预测

将支持向量机应用在失业率预测中,采用遗传算法对传统的支持向量机进行改进,并以福建省城镇登记失业率为对象进行仿真和预测,其结果表明,该模型具有较好的学习和泛化能力,为失业率的预测提供了一条新的途径。     

三维多箱模型预测大气环境质量

建立了16个风向下的三维多箱模式,该模式充分考虑了污染源的空间变化、气象条件和周围环境对污染扩散的影响,以及干沉积和化学转化等物理化学机理对SO2的去除作用,根据质量守恒定律,得到了箱体方程,最后将该模式用于石家庄市区SO2质量浓度的预测,并对其进行了验证,结果表明多箱模型是预测市区和经济开发区的大气质量最佳方法之一。     

基于时间序列的BP神经网络预测鄂尔多斯市的GDP

国内生产总值(GDP)是制定经济发展战略、规划、年度计划以及各种宏观经济政策的重要依据,研究GDP预测方法具有重要的现实意义。在对鄂尔多斯市GDP数据序列分析的基础上,考虑到BP神经网络可以通过简单非线性函数的复合能够实现对于复杂函数的映射,本文采用BP神经网络方法来预测鄂尔多斯市的GDP,取得了较好的预测效果。     

基于遗传算法优化混沌神经网络的股票指数预测

为提高BP神经网络预测模型对混沌时间序列的预测准确性,提出一种基于遗传算法优化BP神经网络的改进混沌时间序列预测方法。本文采用时间序列输入输出参数数量构造BP神经网络拓扑结构,利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型求得最优解,将该预测方法应用到上证综合指数的时间序列进行有效性验证,结果表明了该方法对上证综合指数具有更好的非线性拟合能力和更高的预测准确性。     

股价波动序列的综合预测法研究

股票市场中股票价格的波动是一个非线性混沌时间序列,其参数是随时间变化的。笔者提出的多层递阶一灰色预测综合预测法是运用多层递阶法,通过辨识时变参数,建立时变参数动态预测模型,并在此基础上进一步运用灰色预测方法,通过对时变参数的预测来预测股票价格的波动。实例表明：多层递阶一灰色预测综合预测法有较好的预测精度。     

我国非制造业上市公司财务失败预警模型的实证研究

财务失败预警模型在世界范围内的应用已经很广泛了,但其在我国却仍处于研究探索阶段.笔者对目前在实践中最常用的Altman非制造业上市公司财务失败预警模型(Z3模型)在我国上市公司中的应用进行了实证研究,并对其研究过程及结果进行了分析、总结和评价,最后根据其在我国的实际应用中所存在的问题提出了一些修改建议并通过具体的实例研究对修改结果做了进一步的验证。     

基于BP网络对我国通货膨胀率的预测及其分析

本文首先由我国的CPI计算出通货膨胀率,综合分析了我国通货膨胀率和GDP增长率数据。其次,用带有动量项和自适应学习率的BP神经网络预测出2008～2009年我国CPI,从而推算出2008～2009年我国通货膨胀率分别是4.99%和4.91%左右。并提出了一些相应的政策建议。     

Forecasting the business cycle using survey data

Regular business survey data are published as percentages of firms predicting higher, equal or lower values of some reference variable. Time series of such percentages do not fit production data too well. Univariate models often produce forecasts which are just as accurarate. Still, surveys contain anticipative judgement which, when combined with univariate modeling and proper filtering, may produce a good indicator for business cycle turning points. The way survey data are transformed so as to fit statistics on production seems not to be of much importance. A case study of the Finnish forest industry is offered as an example.