基于神经网络的多过失误差侦破方法的研究

基于神经网络的过失误差侦破方法具有简单、计算量小和适于在线应用的优点,并且相对于传统方法具有处理非线性问题能力较强的特点。但是在侦破多过失误差时,现有的直接侦破法和序列侦破法的侦破率较低。针对这一情况,本文提出了将神经网络和测量数据检验法相结合的侦破多过失误差的新方法,该方法首先利用神经网络较强的鲁棒性和容错能力对数据进行处理,然后再进行过失误差侦破。实例研究表明,这种方法能够有效地提高多过失误差共存时的侦破能力。     

基于改进PCA的软测量数据过失误差侦破

为提高软测量的模型精度,剔除建模数据中的过失误差,提出采用Bagging-PCA方法进行误差侦破。利用Bagging算法的集成思想,改善单变量大误差对经典PCA的影响,提高算法稳定性,实现数据的过失误差侦破。用该方法对丙烯浓度的软测量进行过失误差侦破,取得了良好的效果。     

基于3MAD-PCA的软测量数据过失误差侦破

经典PCA是一种对软测量建模数据进行误差侦破的方法,但当数据中存在单变量大误差时,该方法不能准确确定主元(PC),从而影响了误差侦破效果.针对这一情况,结合单变量误差侦破技术提出了3MAD-PCA方法.该方法首先用3MAD对数据分别进行单变量误差侦破,再利用经典PCA进行多变量误差侦破,提高了经典PCA方法的稳定性,有效实现了数据的过失误差侦破.用该方法对丙烯浓度的软测量数据进行过失误差侦破,取得了良好的效果.     

MT-GLR在甲醇工厂数据协调中的应用

对用于过失误差侦破的MT-GLR联合算法进行了研究.改进后的算法利用MT法和GLR法联合确定可能含有过失误差的集合,综合利用这两种方法,使侦破所有过失误差的可能性大大增加.给出了该算法的设计思想及实施步骤,基于该算法并以甲醇工艺过程为背景,设计了一套实时数据协调和校正软件.该软件在某大型甲醇工厂中投入在线运行,在数据校正及过失误差侦破方面获得了良好的效果.     

用于过失测量数据侦破与校正的改进MT-NT算法

介绍了一种用于过失误差侦破和校正改进的MT-NT算法。改进后的算法采用逐次侦破、校正的策略,有效地解决了在侦破过失误差过程中出现的系数矩阵降秩问题,减少了运算量,增加了信息的可用性和完整性。给出了该算法的框图及步骤,并采用面向对象的方法和C 十语言编制出了过程测量数据校正软件。经过实例验证,该算法可有效侦破测量数据中的过失误差,避免了在运算过程中出现的系数矩阵降秩问题,具有一定的实用性。     

互信息、冗余与广义似然比研究

统计学里有很多描述变量间相关性的方法,大部分都要求随机变量必须服从某一或某些概率分布,要么就是满足一定的假设。互信息是基于熵来测量随机变量间的相关性的,它不需要随机变量满足任何特定分布亦或者是特殊的前提假设。一些研究中,冗余也已经作为一种类似于互信息的方法用以评价变量间的相互关系。对冗余和互信息的概念进行深入研究,并使之用以多维的分类数据。研究发现,在几种独立对数线性模型下,分类数据的互信息和冗余可以表示为广义似然比的函数。广义似然比对样本容量是非常敏感的,但是分类数据的互信息和冗余却并不取决于样本容量而是取决于单元概率。因此互信息和冗余可以用作评价分类数据间的相关关系,既不需要特殊的前提假设又不受样本容量的影响。通过示例验证,针对多维数据,冗余又优于互信息。     

数据校正在焦化碳一过程中的应用

化工生产过程中，开展测量数据的误差侦破与校正方法的研究具有十分重要的意义。针对焦化碳一过程中测量变量的稳态数据校正，采用残差检验方法进行数据协调和过失误差的识别、侦破，通过两层次变换进行数据分类，从而消除了已有算法中出现奇异矩阵的情况。校正后的变量满足物料和元素平衡要求，误差侦破的结果为仪表故障的排除等提供了参考依据；数据校正则提高了生产效率。     

典型炼油装置数据校正技术的研究

化工过程测虽数据作为反映装置运行状况的特征信息,是实现计算机过程控制、模拟、优化和生产管理的基本依据.研究过程数据校正技术,对实现装置优化控制与管理具有重要理论意义和现实意义.现有理论研究大都采用传统统计检验和线性化处理方法,在实际应用有较大局限性.本文在对已有数据校正技术分析的基础上,提出将修正的时间序列分析法用于测量数据校正.综合考虑数据的窄间冗余和时间冗余,充分利用过程的历史数据,建立了时间序列概率模型,并针对含随机误差数据和含过失误差数据两种情况,从时序法平均值、阶跃过程模型等方面详细探讨数据校正方法.将新的数据校正方法用于典型常减压蒸馏装置,结果表明,新方法能够侦破出数据中含有的过失误差;校正值与真值的平均偏差非常小,具有足够的精度保证数据的准确性;修正的时间序列分析法用于数据校正能克服传统方法的局限性.     

稳态在线数据校正在炼油厂气体分离装置上的应用

文章系统地研究了稳态过程在线数据校正技术,在具体实现中采用均值法进行稳态检测,修正系数法进行误差的侦破、识别,通过两层次变换进行数据分类。开发了稳态过程在线数据校正软件,并将校正后的数据作为输入值用于某炼油厂气体分离系统产品质量的在线预测,应用结果表明,采用校正后的数据作为输入值进行产品质量在线预测比直接用原始数据更稳定、更符合实际情况。     

基于广义似然比的频谱感知算法

基于拟合度检验的频谱感知算法(GOF)具有较好的检测性能但复杂度高.给出了新的检测模型,引入广义似然比,利用接收信号样本均值的平方构造了新的检验统计量;推导了该检验统计量的概率密度函数(PDF)和判决门限的表达式,进而实现频谱感知.在高斯信道环境下,与GOF算法及能量检测算法(ED)进行比较,仿真表明:所提算法具有较好的检测性能且复杂度低.     

Estimates of error probability for complex Gaussian channels with generalized likelihood ratio detection

We derive approximate expressions for the probability of error in a two-class hypothesis testing problem in which the two hypotheses are characterized by zero-mean complex Gaussian distributions. These error expressions are given in terms of the moments of the test statistic employed and we derive these moments for both the likelihood ratio test, appropriate when class densities are known, and the generalized likelihood ratio test, appropriate when class densities must be estimated from training data. These moments are functions of class distribution parameters which are generally unknown so we develop unbiased moment estimators in terms of the training data. With these, accurate estimates of probability of error can be calculated quickly for both the optimal and plug-in rules from available training data. We present a detailed example of the behavior of these estimators and demonstrate their application to common pattern recognition problems, which include quantifying the incremental value of larger training data collections, evaluating relative geometry in data fusion from multiple sensors, and selecting a good subset of available features.     

Online reduced gaussian process regression based generalized likelihood ratio test for fault detection

In this paper we consider a new fault detection approach that merges the benefits of Gaussian process regression (GPR) with a generalized likelihood ratio test (GLRT). The GPR is one of the most well-known machine learning techniques. It is simpler and generally more robust than other methods. To deal with both high computational costs for large data sets and time-varying dynamics of industrial processes, we consider a reduced and online version of the GPR method. The online reduced GPR (ORGPR) aims to select a reduced set of kernel functions to build the GPR model and apply it for online fault detection based on GLRT chart. Compared with the conventional GPR technique, the proposed ORGPR method has the advantages of improving the computational efficiency by decreasing the dimension of the kernel matrix. The developed ORGPR-based GLRT (ORGPR-based GLRT) could improve the fault detection efficiency since it is able to track the time-varying characteristics of the processes. The fault detection performance of the developed ORGPR-based GLRT method is evaluated using a Tennessee Eastman process. The simulation results show that the proposed method outperforms the conventional GPR-based GLRT technique.     

Distribution-based anomaly detection via generalized likelihood ratio test: A general Maximum Entropy approach

We address the problem of detecting “anomalies” in the network traffic produced by a large population of end-users following a distribution-based change detection approach. In the considered scenario, different traffic variables are monitored at different levels of temporal aggregation (timescales), resulting in a grid of variable/timescale nodes. For every node, a set of per-user traffic counters is maintained and then summarized into histograms for every time bin, obtaining a timeseries of empirical (discrete) distributions for every variable/timescale node. Within this framework, we tackle the problem of designing a formal Distribution-based Change Detector (DCD) able to identify statistically-significant deviations from the past behavior of each individual timeseries.     

Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order

The finite-sample size and power properties of bootstrapped likelihood ratio system cointegration tests are investigated via Monte Carlo simulations when the true lag order of the data generating process is unknown. Recursive bootstrap schemes are employed which differ in the way in which the lag order is chosen. The order is estimated by minimizing different information criteria and by combining the corresponding order estimates. It is found that, in comparison to the standard asymptotic likelihood ratio test based on an estimated lag order, bootstrapping can lead to improvements in small samples even when the true lag order is unknown, while the power loss is moderate.     

A generalized Shiryayev sequential probability ratio test forchange detection and isolation

The authors derive an online multiple hypothesis Shiryayev sequential probability ratio test (SSPRT) by adopting a dynamic programming approach. It is shown that for a certain criterion of optimality, this generalized Shiryayev SPRT detects and isolates a change in hypothesis in the conditionally independent measurement sequence in minimum time, unlike the Wald SPRT, which assumes the entire measurement sequence to correspond to a single hypothesis. The measurement cost, the cost of a false alarm, and the cost of a miss-alarm are considered in our dynamic programming analysis. The algorithm is shown to be optimal in the infinite time case. Finally, the performance of the algorithm is evaluated by using a few examples. In particular, they implement the algorithm in a fault detection and identification scheme for advanced vehicle control systems     

The marginalized likelihood ratio test for detecting abrupt changes

The generalized likelihood ratio (GLR) test is a widely used method for detecting abrupt changes in linear systems and signals. In this paper the marginalized likelihood ratio (MLR) test is introduced for eliminating three shortcomings of GLR while preserving its applicability and generality. First, the need for a user-chosen threshold is eliminated in MLR. Second, the noise levels need not be known exactly and may even change over time, which means that MLR is robust. Finally, a very efficient exact implementation with linear in time complexity for batch-wise data processing is developed. This should be compared to the quadratic in time complexity of the exact GLR     

Maximum likelihood ratio test for the stability of sequence of Gaussian random processes

Based on the Karhunen-Loeve expansion, the maximum likelihood ratio test for the stability of sequence of Gaussian random processes is investigated. The likelihood function is based on the first p scores of eigenfunctions in the Karhunen-Loeve expansion for Gaussian random processes. Though the scores are unobservable, we show that the effect of the difference between scores and their estimators is negligible as the sample size tends to infinity. The asymptotic distribution is proved to be the Gumbel extreme value distribution. Under the alternative the test is shown to be consistent. For different choices of p, simulation results show that the test behaves quite well in finite samples. The test procedure is also applied to the annual temperature data of central England. The results show that the temperatures have risen in the last twenty years, however there is no evidence to show that the autocovariance functions of the temperatures have changed among the range of the observations.     

Generalized likelihood ratio test for varying-coefficient models with different smoothing variables

Varying-coefficient models are popular multivariate nonparametric fitting techniques. When all coefficient functions in a varying-coefficient model share the same smoothing variable, inference tools available include the F-test, the sieve empirical likelihood ratio test and the generalized likelihood ratio (GLR) test. However, when the coefficient functions have different smoothing variables, these tools cannot be used directly to make inferences on the model because of the differences in the process of estimating the functions. In this paper, the GLR test is extended to models of the latter case by the efficient estimators of these coefficient functions. Under the null hypothesis the new proposed GLR test follows the χ²-distribution asymptotically with scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Further, we have derived its asymptotic power which is shown to achieve the optimal rate of convergence for nonparametric hypothesis testing. A simulation study is conducted to evaluate the test procedure empirically.     

The likelihood ratio test for hidden Markov models in two-sample problems

The asymptotic distribution of the likelihood ratio test statistic in two-sample testing problems for hidden Markov models is derived when allowing for unequal sample sizes as well as for different families of state-dependent distributions. In both cases under regularity conditions the limit distribution is a standard χ²-distribution, and in particular does not depend on the ratio of the distinct sample sizes. In a simulation study, the finite sample properties are investigated, and the methodology is illustrated in an application to modeling the movement of Drosophila larvae.     

Generalized likelihood ratio test for voiced-unvoiced decision in noisy speech using the harmonic model

In this paper, a novel method for voiced-unvoiced decision within a pitch tracking algorithm is presented. Voiced-unvoiced decision is required for many applications, including modeling for analysis/synthesis, detection of model changes for segmentation purposes and signal characterization for indexing and recognition applications. The proposed method is based on the generalized likelihood ratio test (GLRT) and assumes colored Gaussian noise with unknown covariance. Under voiced hypothesis, a harmonic plus noise model is assumed. The derived method is combined with a maximum a-posteriori probability (MAP) scheme to obtain a pitch and voicing tracking algorithm. The performance of the proposed method is tested using several speech databases for different levels of additive noise and phone speech conditions. Results show that the GLRT is robust to speaker and environmental conditions and performs better than existing algorithms.