Kalman filtering of a space-time Markov random field

In this paper, finite-dimensional recursive filters for space-time Markov random fields are derived. These filters can be used with the expectation maximization (EM) algorithm to yield maximum likelihood estimates of the parameters of the model.     

四种多变量校准方法在FTIR多组分分析中的性能比较

本文对四种多变量校准方法－－经典最小二乘法（CLS），偏最小二乘法（PLS），卡尔曼滤波法（KFM）以及人工神经网络法（ANN）－－在多组分浓度分析方面的性能进行了比较。选择五种红外谱图严重混叠的大气有机毒物－－1，3－丁二烯，苯，邻二甲苯，氯苯和丙烯醛－－作为分析对象。分别计算各种方法对该5组分体系的平均预测误差MPE和平均相对误差MRE进行比较。结果表明，偏最小二乘法在处理这类问题中是最稳健的方法。     

Automatic detection and identification of shocks in Gaussian state‐space models: a Bayesian approach

An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in an univariate linear Gaussian state–space model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. In addition, this algorithm incorporates the possibility of eliminating earlier interventions when subsequent evidence against them comes to light. Finally, the procedure is illustrated with two empirical examples taken from the literature. Copyright © 2005 John Wiley & Sons, Ltd.     

卡尔曼滤波紫外光度法同时测定酱油、食醋中的山梨酸和苯甲酸

在酸性条件下，采用乙酸乙酯萃取、Na2HPO4溶液反萃取分离酱汪、食醋中的山梨酸、苯甲酸，卡尔曼滤波法计算结果，并与比值导数波谱法进行比较。结果表明，卡尔曼滤波法较地数波谱法有较高准确度和精密度，并能监测干扰物质存在与否。用于实际样品分析有更高的可靠性。     

加权增量卡尔曼滤波法在ICP－AES中的应用

卡尔曼滤波法用于ＩＣＰ－ＡＥＳ分析时，在白色噪音情况下能给出准确的分析结果，当试样的某些组分未知时，测量噪音将不是白色的，分析结果将不准确。本文提出了一种加权增量卡尔曼滤波法来解决这个问题。     

混合食用色素的卡尔曼滤波光度分析

本文用联机卡尔曼滤波光度法成功地同时测定了混合合成食用色素样品中五个组分。它们分别是40号红、苋菜红、柠檬黄、日落黄和亮蓝。方法准确、快速,无需予分离。     

卡尔曼滤波紫外光度法同时测定饮料中苯甲酸及糖精的研究

在酸性条件下，用乙醚萃取、蒸干乙醚后，用ＮａＨＣＯ３溶解残渣，从复杂的样品中分离出苯甲酸和糖精，测定它们在２４５～２８１ｎｍ之间的紫外吸收，运用卡尔曼滤波算法计算两者的浓度，用新息序列监测干扰物质分离的程度。此法简化了许多分离手续，用于饮料分析，结果满意。     

Initial data truncation for multivariate output of discrete-event simulations using the Kalman filter

Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. An algorithm for determining the appropriate initial-data truncation point for multivariate output is proposed. The technique entails averaging across independent replications and estimating a steady-state output model in a state-space framework. A Bayesian technique called Multiple Model Adaptive Estimation (MMAE) is applied to compute a time varying estimate of the output's steady-state mean vector. This MMAE implementation features the use, in paralle, of a bank of Kalman filters. Each filter is constructed under a different assumption concerning the output's steady-state mean vector. One of the filters assumes that the steady-state mean vector is accurately reflected by an estimate, called the assumed steady-state mean vector, taken from the last half of the simulation data. As the filters process the output through the effective transient, this particular filter becomes more likely (in a Bayesian sense) to be the best filter to represent the data and the MMAE mean estimator is influenced increasingly towards the assumed steady-state mean vector. The estimated truncation point is selected when a norm of the MMAE mean vector estimate is within a small tolerance of the assumed steady-state mean vector. A Monte Carlo analysis using data from simulations of open and closed queueing models is used to evaluate the technique. The evaluation criteria include the ability to construct accurate and reliable confidence regions for the mean response vector based on the truncated sequences.     

卡尔曼滤波析相光度法同时测定锰铁铜锌镉的研究——5-Br-PADAP—Triton X-100体系

运用卡尔曼滤波递推法,以2-(5-溴-2-吡啶偶氮)—5-二乙氨基苯酚(5-Br-PADAP)为显色剂,建立了同时测定锰、铁、铜、锌、镉析相光度法.在pH9.0硼酸-氢氧化钠介质中,胶束溶液在95℃加热1h,配合物被Triton X-100相完全富集,最大吸收峰分别为锰566nm、铁556nm、铜560nm、锌562nm、镉557nm,工作曲线范围除镉为0～8μg/5ml外,其余均为0～10μg/5ml.应用于大米中锰铁铜锌镉的同时测定,结果满意.     

改进的单纯形优化法用于混合稀土的同时测定

本文应用改进的单纯形优化法较好地弥补了目前在分析化学计量学中得到广泛应用的卡尔曼滤波法所存在的不足，交法应用于混合稀土的同时测定中，获得了满意的结果，结果准确度较卡尔滤波法的有显著的提高。