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Identification of Hammerstein-Wiener models

AUTOMATICA 2013年第1期49卷 70-81页

作者： Wills, Adrian Schon, Thomas B. Ljung, Lennart Ninness, Brett Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia Linkoping Univ Div Automat Control SE-58183 Linkoping Sweden

This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein-Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and colored stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： System identification Hammerstein Wiener Block-oriented models Nonlinear models Dynamic systems Monte Carlo method Smoothing expectation maximization algorithm Particle methods Maximum Likelihood

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Pricing fraud detection in online shopping malls using a finite mixture model

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2013年第3期12卷 195-207页

作者： Kim, Kwanho Choi, Yerim Park, Jonghun Seoul Natl Univ Dept Ind Engn Seoul 151744 South Korea

Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection performances. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Pricing fraud Fraud detection Online shopping e-Commerce expectation maximization algorithm Finite mixture model

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Estimation of 1-bit quantized time-series with Markov regime

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SIGNAL PROCESSING 1997年第3期58卷 273-292页

作者： Logothetis, A Krishnamurthy, V Poor, HV PRINCETON UNIV DEPT ELECT ENGNPRINCETONNJ 08544

In this paper we consider de-interleaving a finite number of stochastic parametric sources. The sources are modeled as independent autoregressive (AR) processes. Based on a Markovian switching policy, we assume that the different sources transmit signals on the same single channel, The receiver records the 1-bit quantized version of the transmitted signal and aims to identify the sequence of active sources. Once the source sequence has been identified, the characteristics (parameters) of each source are estimated. We formulate the parametric pulse train de-interleaving problem as a 1-bit quantized Markov modulated AR series, The algorithm proposed in this paper combines Hidden Markov Model (HMM) and Binary Time Series (BTS) estimation techniques. Our estimation scheme generalizes Kedem's (1980) binary time series algorithm for linear time series to Markov modulated time series. (C) 1997 Elsevier Science B.V.

关键词： expectation maximization algorithm autoregressive process hidden Markov models parameter estimation binary time series

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expectation maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing

arXiv

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arXiv 2024年

作者： Rashid, Mohammed Nanzer, Jeffrey A. The Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particularly at low signal-to-noise ratios (SNRs), for improved detection capability. This is due to the fact that it uses the energy only from the present sensing interval for the PU detection. Previous studies have shown that even with fewer observed samples per energy statistics, improved detection capabilities can be achieved by aggregating both present and past ED samples in a test statistic. Thus, a weighted sequential energy detector (WSED) has been proposed, but it is based on aggregating all the collected ED samples over an observation window. For a highly dynamic PU over the consecutive sensing intervals, that involves also combining the outdated samples in the test statistic that do not correspond to the present state of the PU. In this paper, we propose a modified WSED (mWSED) that uses the primary user states information over the window to aggregate only the highly correlated ED samples in its test statistic. In practice, since the PU states are a priori unknown, we also develop a joint expectation-maximization and Viterbi (EM-Viterbi) algorithm based scheme to iteratively estimate the states by using the ED samples collected over the window. The estimated states are then used in mWSED to compute its test statistics, and the algorithm is referred to here as the EM-mWSED algorithm. Simulation results show that EM-mWSED outperforms other schemes and its performance improves by increasing the average number of neighbors per SU in the network, and by increasing the SNR or the number of samples per energy statistic. Copyright © 2024, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Parameter estimation for nonlinear systems with multirate measurements and random delays

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AICHE JOURNAL 2021年第9期67卷 e17327-e17327页

作者： Han, Ruijing Salehi, Yousef Huang, Biao Prasad, Vinay Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

We present an approach for parameter estimation with multirate measurements, with the slow measurements having variable time delays due to laboratory analysis, and also being functions of all the states during the sample collection. We formulate a particle filter-based approach under the framework of the expectation maximization algorithm to develop the estimates. The effectiveness and applicability of the proposed method are demonstrated though a simulation example, a hybrid tank experiment and an industrial case study;in each case, the slow and fast measurements are for the same variable. We show that this approach results in improved parameter estimation when the information from the delayed measurements is fused with the fast measurement information.

关键词： delay estimation expectation maximization algorithm multirate measurements parameter estimation particle filter

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Prediction of Slope Stability using Naive Bayes Classifier

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第3期22卷 941-950页

作者： Feng, Xianda Li, Shuchen Yuan, Chao Zeng, Peng Sun, Yang Univ Jinan Sch Civil Engn & Architecture Jinan Shandong Peoples R China Shandong Univ Geotech Struct Engn Res Ctr Jinan Shandong Peoples R China Univ Grenoble Alpes Lab 3SR Grenoble France Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu Sichuan Peoples R China

Slope stability prediction is of primary concern in identifying terrain that is susceptible to landslides and mitigating the damages caused by landslides. In this study, a Naive Bayes Classifier (NBC) was employed to predict slope stability for a slope subjected to circular failures, based on six input factors: slope height (H), slope angle (alpha), cohesion (c), friction angle (phi), unit weight (gamma), and pore pressure ratio (r (u) ). An expectation maximization algorithm was used to perform parameter learning for the NBC with an incomplete data set of 69 slope cases. The model validation with 13 new cases shows that, when compared to the existing empirical approach, the proposed NBC model yields better performance in terms of both accuracy and applicability (i.e., the NBC allows us to determine the probability of slope stability based on any subset of the six input factors).

关键词： slope stability naive bayes classifier incomplete data expectation maximization algorithm circular failures

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Comparison of Deep Learning and Particle Smoother expectation maximization Methods for Estimation of Myocardial Perfusion PET Kinetic Parameters

arXiv

引用

arXiv 2024年

作者： Chin, Myungheon Zou, Sarah J. Chinn, Garry Levin, Craig S. Department of Electrical Engineering Stanford University United States Department of Radiology Stanford University United States Stanford University United States

Background: Positron emission tomography (PET) is widely used for studying dynamic processes, such as myocardial perfusion, by acquiring data over time frames. Kinetic modeling in PET allows for the estimation of physiological parameters, offering insights into disease characterization. Conventional approaches have notable limitations;for example, graphical methods may reduce accuracy due to linearization, while non-linear least squares (NLLS) methods may converge to local minima. Purpose: This study aims to develop and validate two novel methods for PET kinetic analysis of 82Rb: a particle smoother-based algorithm within an expectation-maximization (EM) framework and a convolutional neural network (CNN) approach. Methods: The proposed methods were applied to simulated 82Rb dynamic PET myocardial perfusion studies. Their performance was compared to conventional NLLS methods and a Kalman filter-based expectation-maximization (KEM) algorithm. Results: The success rates for parameters F, k3, and k4 were 46.0%, 67.5%, and 54.0% for the particle smoother with EM (PSEM) and 86.5%, 83.0%, and 79.5% for the CNN model, respectively, outperforming the NLLS method. Conclusions: The CNN and PSEM methods showed promising improvements over traditional methods in estimating kinetic parameters in dynamic PET studies, suggesting their potential for enhanced accuracy in disease characterization. © 2024, CC BY.

关键词： expectation maximization algorithm

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Improved Parameter Estimation with Threshold Adaptation of Cognitive Local Sensors

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2012年第5期14卷 471-480页

作者： Seol, Dae-Young Lim, Hyoung-Jin Song, Moon-Gun Im, Gi-Hong Pohang Univ Sci & Technol Dept Elect & Elect Engn Pohang 790784 South Korea

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

关键词： Cognitive radio cooperative spectrum sensing expectation maximization algorithm likelihood ratio test unsupervised learning

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Exact reduced-complexity maximum likelihood reconstruction of multiple 3-d objects from unlabeled unoriented 2-d projections and electron microscopy of viruses

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2007年第12期16卷 2865-2878页

作者： Lee, Junghoon Doerschuk, Peter C. Johnson, John E. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Scripps Res Inst Dept Mol Biol La Jolla CA 92037 USA

In cryo-electron microscopy, the data is comprised of noisy 2-D projection images of the 3-D electron scattering intensity of the object where the orientation of the projections is unknown. Often, the images show randomly selected objects from a mixture of different types of objects. Objects of different type may be unrelated, e.g., different species of virus, or related, e.g., different conformations of the same species of virus. Due to the low SNR and the 2-D nature of the data, it is challenging to determine the type of the object shown in an individual image. A statistical model and maximum likelihood estimator that computes simultaneous 3-D reconstruction and labels using an expectation maximization algorithm exists but requires extensive computation due to the numerical evaluation of 3-D or 5-D integrations of a square matrix of dimension equal to the number of degrees of freedom in the 3-D reconstruction. By exploiting the geometry of rotations in 3-D, the estimation problem can be transformed so that the inner-most numerical integral has a scalar rather than a matrix integrand. This leads to a dramatic reduction in computation, especially as the number of degrees of freedom in the 3-D reconstruction increases. Numerical examples of the 3-D reconstructions are provided based on synthetic and experimental images where the objects are small spherical viruses.

关键词： cryo-electron microscopy (cryo EM) expectation maximization algorithm maximum likelihood estimation structural biology 3-D signal reconstruction tomography virology virus

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Neural networks for predicting conditional probability densities: Improved training scheme combining EM and RVFL

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NEURAL NETWORKS 1998年第1期11卷 89-116页

作者： Husmeier, D Taylor, JG Univ London Kings Coll Dept Math London WC2R 2LS England

Predicting conditional probability densities with neural networks requires complex (at least two-hidden-layer) architectures, which normally leads to rather long training times. By adopting the RVFL concept and constraining a subset of the parameters to randomly chosen initial values (such that the EM-algorithm can be applied), the training process can be accelerated by about two orders of magnitude. This allows training of a whole ensemble of networks at the same computational costs as would be required otherwise for training a single model. The simulations performed suggest that in this way a significant improvement of the generalization performance can be achieved. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： automatic relevance determination conditional probability density expectation maximization algorithm gaussian mixture model generalization performance maximum likelihood model complexity network committees random vector functional link net approach time series prediction

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