多传感器多变量AR模型信息融合辨识方法

对于带白色公共干扰噪声、白色观测噪声和传感器偏差的多传感器多变量自回归（AR）模型,当AR模型参数、传感器偏差和噪声方差未知时,提出了一种信息融合多段辨识方法,其中用多重递推增广最小二乘法（MRELS）得到AR模型参数和传感器偏差的局部和融合估值器,再用相关方法得到局部和融合噪声方差估值器。这些估值器具有一致性。一个仿真例子验证了其有效性。     

非平稳随机过程五阶累积量计算的简化算法

四次相位耦合是水下目标辐射噪声非线性的重要特征,五阶累积量正是提取这些特征的必要工具,但五阶累积量计算复杂,难以工程实现。为了解决这一问题,研究了非平稳随机过程五阶累积量递推计算与计算量的关系,提出了五阶累积量计算的简化算法。理论分析与仿真表明：五阶累积量计算的简化算法能大幅度地减小计算量,通过简化计算得到的五阶累积量仍有良好的抑制高斯噪声性能,这为高阶统计量的工程应用提供了一种有效手段。     

Hammerstein OEMA系统的辅助模型最小二乘辨识

针对Hammerstein 输出误差自回归(OEMA)模型, 将关键变量分离原理与辅助模型辨识思想相结合,提出了基于关键变量分离的辅助模型递推增广最小二乘辨识方法.该方法能获得系统参数估计和噪声参数估计,且能实现在线辨识.     

多维和多重递推辅助变量辨识算法

对带白色观测噪声的多维自回归(AR)模型,应用相关方法,分别提出了多维和多重递推辅助变量(RIV)算法,给出了未知AR参数的强一致估计;推广了一维RIV算法。提出了模型噪声和观测噪声方差的信息融合估值器,它们具有强一致性。一个仿真例子说明其有效性。     

Alternative regime switching models for forecasting inflation

US inflation appears to undergo shifts in its mean level and variability. We evaluate the performance of three useful models for capturing such shifts. The models studied are the Markov switching models, state space models with heavy‐tailed errors, and state space models with compound error distributions. Our study shows that all three models have very similar performance when evaluated in terms of the mean squared or mean absolute forecast errors. However, the latter two models are considerably more parsimonious, and easily beat the more profligately parameterized Markov switching models in terms of model selection criteria, such as the AIC or the SBC. Thus, these may serve as useful continuous alternatives to the popular discrete Markov switching models for capturing shifts in time series. Copyright © 2001 John Wiley & Sons, Ltd.     

Predicting bankruptcy using recursive partitioning and a realistically proportioned data set

Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to ‘blow the whistle’ in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two‐variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981–1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33–58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model. Copyright © 2000 John Wiley & Sons, Ltd.     

离散经典风险模型破产概率的递推解及算法实现与比较

该文重点研究完全离散经典风险模型任意初始盈余下最终破产概率的递推解,并结合中国保险业具体数据加以分析,在Matlab中实现了算法的应用和数据比较．     

CARMA模型的多新息增广随机梯度辨识算法

将多新息辨识理论用于研究CARMA模型参数辨识问题,通过把标量新息扩展为向量新患,即多新息,得出相应的多新息增广随机梯度辨识算法.仿真结果验证了提出算法的有效性.     

关系型数据库BOM表的遍历算法的改进及实现

定义了单层BOM和多层BOM树的结构,并总结了实际运用中的多级型BOM遍历的两种基本算法:递归查找法、分层查找法.在介绍这两种算法的原理和对其速度、资源占用、实用性进行评价的基础上,提出了一种综合以上两种算法的优点,且适合普遍使用的关系型数据库存储的改进的多级型BOM遍历算法,使该改进算法包含速度快、资源占用低、实用性高的优点.同时详细介绍了此算法的软件实现,并且在实际的运用中取得了明显的效果.     

改进的数字匹配滤波器的设计及FPGA实现

提出一种改进的数字匹配滤波器(digital matchedfilter,DMF)结构。在发送端用2个码元长度为N的同一伪随机序列重复对一个基带数据码元进行整周期扩频,替代常规的用一个码元长度为2N的伪随机序列对一个基带数据码元进行整周期扩频。在接收端只需要用码元长度为N的DMF来实现解扩,将同一个基带数据码元内出现的两个相关峰叠加后输出即可。分析并仿真了改进的DMF的检测概率和虚警概率。基于递归折叠原理实现了改进的DMF结构。实验结果表明,在相同扩频增益的条件下,该改进结构与基本DMF的检测概率和虚警概率基本相同,而与递归折叠结构相比,该改进结构大大节约了硬件资源。