基于非线性修正策略的空气质量预警系统研究

建立科学、有效的空气质量预警系统,对于保护人们的身体健康和促进社会和谐稳定具有重要的科学价值和实际意义.为此,本文首先利用孤立森林(isolationforest,iForest)算法进行空气质量指数(air quality index,AQI)离群点分析,然后建立了一种空气质量预警系统,该系统由数据预处理模块、优化模块、预测模块和修正模块构成,融合了时变滤波经验模态分解(time varying filtering based empirical mode decomposition,TVF-EMD)、改进的蝴蝶优化算法(modified butterfly optimization algorithm,MBOA)、离群鲁棒极限学习机(outlier robust extreme learning machine,ORELM)和非线性修正策略,该预警系统成功地实现了空气质量的有效预警.同时,以污染程度不同的5个城市作为实验地点对预警效果进行检验,结果表明:1)与经验模态分解(empirical mode decomposition,EMD)相比,TVF-EMD可以更为有效地降低原始数据的非线性和非平稳性特征;2)基于MBOA的误差非线性修正策略比其他误差修正策略更胜一筹,可以显著提高预警系统的性能;3)建立的预警系统的性能要优于其他对比模型,可以对污染程度不同的城市进行有效预警.     

R~(n+m)中极小子流形的刚性定理

研究欧氏空间中不具有平坦法丛的一类特殊极小子流形,利用积分估计方法,证明了若它的第二基本形式满足一些衰减条件,则它是一个仿射平面.     

沪深股市和香港股市的风险溢出效应研究——基于时变ΔCoVaR模型的分析

本文基于时变ΔCoVaR模型,对2006年11月至2018年12月间沪深股市和香港股市的尾部风险溢出效应进行了估算.研究发现:1)沪深股市与香港股市之间存在着双向风险溢出效应,且溢出效应均为正;2)香港股市对沪深股市的风险溢出效应强于沪深股市对香港股市的风险溢出效应;3)深市和香港股市之间的风险溢出效应的波动幅度大于沪市和香港股市之间的风险溢出效应的波动幅度;4)沪港通和深港通的开通并没有显著增加香港股市与沪深股市之间的双向风险外溢程度.     

水平力在窄桩台码头排架中分配研究

针对窄桩台高桩码头排架中水平集中力的横向分力分配系数建立新的简化模型,给出模型求解方法.确定水平集中力的横向分力在窄桩台码头各排架中的分配系数时,排架简化为支撑弹簧,码头上部结构在水平方向不能再被视为刚性连续梁,而应该看作柔性连续梁.提出了排架水平刚性系数和弹性连续梁的求解公式,并编制成电子表格,只需要输入码头尺寸和材料参数等,即可直接得到分配系数.对于窄桩台码头,最不利排架可能不再是端部排架,且规范计算得到的最不利分配系数偏小,可能会存在安全隐患.用电子表格计算分配系数可提高设计精度,确保结构安全可靠.研究结果可以为结构设计和规范修订提供参考.     

基于一维梁理论模型的金属单搭粘接贴片接头强度分析

摘要： 为了提高单搭粘接接头的强度,基于Goland和Reissner的一维梁理论模型,设计了单搭粘接贴片接头;对铝合金5052(AA5052)单搭粘接接头、AA5052单搭粘接贴铝片接头和AA5052单搭粘接贴铜片接头进行了拉伸 剪切试验,并通过有限元数值模拟获得3种接头破坏位置附近胶层的应力分布情况.结果表明：随着搭接端部刚度的增大,单搭粘接接头的强度逐渐增大,胶层中间部分的应力变化逐渐趋于平缓;与AA5052单搭粘接接头相比,AA5052单搭粘接贴铝片接头的强度提高了3.8%,AA5052单搭粘接贴铜片接头的强度提高了4.9%,从而验证了采用单搭粘接贴片接头的方法具有较好的可行性和有效性.     

多变时滞不确定中立型系统的滑模控制

研究了不确定中立型系统的滑动模态控制.系统含有多个变时滞和非线性外部干扰项;根据当前状态和时滞状态给出了滑模面的设计,滑模控制器的设计保证了系统的状态轨线在有限时间内被驱动到指定的滑模面上并保持运动;再利用构造Lyapunov函数的方法给出了闭环系统渐进稳定的一个充分条件,充分条件以线性矩阵不等式的形式给出.     

时变海场景双基SAR回波实时模拟方法研究

基于时变海场景双站散射机理,结合双基合成孔径雷达(SAR,Synthetic Aperture Radar)成像几何模型,提出了一种时变海场景双基SAR射频回波信号实时模拟方法。针对时变海场景的复杂耦合散射,采用多径近似理论建立了双基SAR海面目标回波信号模型,提取了时变海面双基SAR系统响应函数,结合高性能计算技术、同步动态随机存取(SDRAM)技术以及全系统交换设计,实现了时变海场景双基SAR射频回波信号实时模拟,最后仿真测试结果验证了该方法的有效性。     

Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?

We study the performance of recently developed linear regression models for interval data when it comes to forecasting the uncertainty surrounding future stock returns. These interval data models use easy‐to‐compute daily return intervals during the modeling, estimation and forecasting stage. They have to stand up to comparable point‐data models of the well‐known capital asset pricing model type—which employ single daily returns based on successive closing prices and might allow for GARCH effects—in a comprehensive out‐of‐sample forecasting competition. The latter comprises roughly 1000 daily observations on all 30 stocks that constitute the DAX, Germany's main stock index, for a period covering both the calm market phase before and the more turbulent times during the recent financial crisis. The interval data models clearly outperform simple random walk benchmarks as well as the point‐data competitors in the great majority of cases. This result does not only hold when one‐day‐ahead forecasts of the conditional variance are considered, but is even more evident when the focus is on forecasting the width or the exact location of the next day's return interval. Regression models based on interval arithmetic thus prove to be a promising alternative to established point‐data volatility forecasting tools. Copyright ©2015 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting the Daily Time‐Varying Beta of European Banks During the Crisis Period: Comparison Between GARCH Models and the Kalman Filter

This intention of this paper is to empirically forecast the daily betas of a few European banks by means of four generalized autoregressive conditional heteroscedasticity (GARCH) models and the Kalman filter method during the pre‐global financial crisis period and the crisis period. The four GARCH models employed are BEKK GARCH, DCC GARCH, DCC‐MIDAS GARCH and Gaussian‐copula GARCH. The data consist of daily stock prices from 2001 to 2013 from two large banks each from Austria, Belgium, Greece, Holland, Ireland, Italy, Portugal and Spain. We apply the rolling forecasting method and the model confidence sets (MCS) to compare the daily forecasting ability of the five models during one month of the pre‐crisis (January 2007) and the crisis (January 2013) periods. Based on the MCS results, the BEKK proves the best model in the January 2007 period, and the Kalman filter overly outperforms the other models during the January 2013 period. Results have implications regarding the choice of model during different periods by practitioners and academics. Copyright © 2016 John Wiley & Sons, Ltd.