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Robust identification approach for nonlinear state-space models

NEUROCOMPUTING 2019年 333卷 329-338页

作者： Liu, Xin Yang, Xianqiang Harbin Inst Technol Sch Astronaut Harbin 150080 Heilongjiang Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Heilongjiang Peoples R China

The identification of nonlinear state-space model (NSSM) with output observations corrupted by outliers is investigated in this paper. The outlier is commonly encountered in practical industrial processes which should not be ignored in nonlinear processes modeling. The statistical scheme based on the Student's t-distribution is applied to resist the outlier and the expectation-maximization (EM) algorithm is employed to simultaneously identify the undetermined model and noise parameters. A particle smoother is introduced and used to approximately calculate the desired Q-function. The usefulness of the proposed approach is demonstrated via the numerical and mechanical examples. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Nonlinear system identification Robustness Student's t-distribution Particle smoother expectation-maximization algorithm

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A Bayesian molecular interaction library

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2003年第7期17卷 435-461页

作者： Rantanen, VV Gyllenberg, M Koski, T Johnson, MS Univ Turku Dept Math FIN-20014 Turku Finland Abo Akad Univ Dept Biochem & Pharm FIN-20521 Turku Finland Linkoping Univ Dept Math S-58183 Linkoping Sweden

We describe a library of molecular fragments designed to model and predict non-bonded interactions between atoms. We apply the Bayesian approach, whereby prior knowledge and uncertainty of the mathematical model are incorporated into the estimated model and its parameters. The molecular interaction data are strengthened by narrowing the atom classification to 14 atom types, focusing on independent molecular contacts that lie within a short cutoff distance, and symmetrizing the interaction data for the molecular fragments. Furthermore, the location of atoms in contact with a molecular fragment are modeled by Gaussian mixture densities whose maximum a posteriori estimates are obtained by applying a version of the expectation-maximization algorithm that incorporates hyperparameters for the components of the Gaussian mixtures. A routine is introduced providing the hyperparameters and the initial values of the parameters of the Gaussian mixture densities. A model selection criterion, based on the concept of a 'minimum message length' is used to automatically select the optimal complexity of a mixture model and the most suitable orientation of a reference frame for a fragment in a coordinate system. The type of atom interacting with a molecular fragment is predicted by values of the posterior probability function and the accuracy of these predictions is evaluated by comparing the predicted atom type with the actual atom type seen in crystal structures. The fact that an atom will simultaneously interact with several molecular fragments forming a cohesive network of interactions is exploited by introducing two strategies that combine the predictions of atom types given by multiple fragments. The accuracy of these combined predictions is compared with those based on an individual fragment. Exhaustive validation analyses and qualitative examples ( e. g., the ligand-binding domain of glutamate receptors) demonstrate that these improvements lead to effective modeling and predic

关键词： combining posterior probabilities expectation-maximization algorithm maximum a posteriori estimates mixture model protein-ligand recognition

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A modified mixture of experts network structure for ECG beats classification with diverse features

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2005年第7期18卷 845-856页

作者： Güler, I Übeyli, ED Gazi Univ Fac Tech Educ Dept Elect & Comp Educ TR-06500 Ankara Turkey TOBB Ekon & Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features. The MME is a modular neural network architecture for supervised learning. expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. The wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the MME network structure for training and testing purposes. We explored the ability of designed and trained MME network structure, combined with wavelet preprocessing (computing wavelet coefficients) and nonlinear dynamics tools (computing Lyapunov exponents), to discriminate five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying long-term ECG signals for early detection of heart diseases/abnormalities. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： modified mixture of experts expectation-maximization algorithm wavelet coefficients Lyapunov exponents ECG beats classification diverse features

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Adaptive Iterative Detectors for Phase-Uncertain Channels via Variational Bounding

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IEEE TRANSACTIONS ON COMMUNICATIONS 2009年第3期57卷 716-725页

作者： Nissila, Mauri Pasupathy, Subbarayan Nokia FIN-90570 Oulu Finland Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.

关键词： Adaptive iterative detection variational Bayesian algorithms expectation-maximization algorithm Bayesian estimation

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Genomewide motif identification using a dictionary model

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PROCEEDINGS OF THE IEEE 2002年第11期90卷 1803-1810页

作者： Sabatti, C Lange, K Univ Calif Los Angeles Dept Human Genet Los Angeles CA 90095 USA Univ Calif Los Angeles Biomath Dept Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA

This paper surveys and extends models and algorithms for identifying binding sites in noncoding regions of DNA. Binding sites control the transcription of genes into messenger RNA in preparation for translation into proteins. The base sequence of most binding sites is not entirely fixed, with the different permitted spellings collectively constituting a "motif." After summarizing the underlying biological issues, we review three different models for binding site identification. Each model was developed with a different type of dataset as reference. We then present a unified model that borrows from the previous ones and integrates their main features. In our unified model, one can identify motifs and their unknown positions along a sequence. One can also fit the model to data using maximum likelihood and maximum a posteriori algorithms. These algorithms rely on recursive formulas and the maximization/minorization principle. Finally, we conclude with a prospectus of future data analyses and theoretical research.

关键词： expectation-maximization algorithm genomic sequence maximum a posteriori text segmentation

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Distributed Sparse Recursive Least-Squares Over Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第6期62卷 1386-1395页

作者： Liu, Zhaoting Liu, Ying Li, Chunguang Shaoxing Univ Dept Elect Engn Shaoxing 312000 Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Distributed estimation over networks has received much attention in recent years due to its broad applicability. Many signals in nature present high level of sparsity, which contain only a few large coefficients among many negligible ones. In this paper, we address the problem of in-network distributed estimation for sparse vectors, and develop several distributed sparse recursive least-squares (RLS) algorithms. The proposed algorithms are based on the maximum likelihood framework, and the expectation-maximization algorithm, with the aid of thresholding operators, is used to numerically solve the sparse estimation problem. To improve the estimation performance, the thresholding operators related to l(0)- and l(1)-norms with real-time self-adjustable thresholds are derived. With these thresholding operators, we can exploit the underlying sparsity to implement the distributed estimation with low computational complexity and information exchange amount among neighbors. The sparsity- promoting intensity is also adaptively adjusted so that a good performance of the sparse solution can be achieved. Both theoretical analysis and numerical simulations are presented to show the effectiveness of the proposed algorithms.

关键词： Wireless sensor network distributed estimation sparsity adaptive signal processing recursive least square expectation-maximization algorithm

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Accelerated life tests for log-normal series system with dependent masked data under Type-I progressive hybrid censoring

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第2期46卷 1628-1646页

作者： Cai, Jing Shi, Yimin Yue, Hongbo Northwestern Polytech Univ Dept Appl Math Xian Peoples R China Guizhou Minzu Univ Coll Sci Guiyang Peoples R China

This article considers the constant stress accelerated life test for series system products, where independent log-normal distributed lifetimes are assumed for the components. Based on Type-I progressive hybrid censored and masked data, the expectation-maximization algorithm is applied to obtain the estimation for the unknown parameters, and the parametric bootstrap method is used for the standard deviation estimation. In addition, Bayesian approach combining latent variable with Gibbs sampling is developed. Further, the reliability functions of the system and components are estimated at use stress level. The proposed method is illustrated through a numerical example under different masking probabilities and censoring schemes.

关键词： Accelerated life tests Dependent masked data expectation-maximization algorithm Gibbs sampling Log-normal distribution Masking probabilities 62N01 62N05 62F15

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Detecting variabilities of Doppler ultrasound signals by a modified mixture of experts with diverse features

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DIGITAL SIGNAL PROCESSING 2008年第2期18卷 267-279页

作者： Ubeyli, Elif Derya Guler, Inan TOBB Ekon Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey Gazi Univ Fac Tech Educ Dept Elect & Comp Educ TR-06500 Ankara Turkey

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of Doppler ultrasound signals with diverse features. The MME is a modular neural network architecture for supervised learning. expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by spectral analysis methods (fast Fourier transform and model-based methods) and classification using the classifiers trained on the extracted features. In order to discriminate the Doppler ultrasound signals, the ability of designed and trained MME network structure combined with spectral analysis methods was explored. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying the Doppler ultrasound signals for early detection of arterial diseases. (C) 2007 Elsevier Inc. All rights reserved.

关键词： modified mixture of experts expectation-maximization algorithm spectral analysis methods Doppler ultrasound signals diverse features

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Misspecification analysis of gamma- and inverse Gaussian-based perturbed degradation processes

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2024年第3期40卷 640-667页

作者： Esposito, Nicola Mele, Agostino Castanier, Bruno Giorgio, Massimiliano Univ Napoli Federico II Naples Italy Univ Campania Luigi Vanvitelli Dipartimento Ingn Aversa Italy Kineton Naples Italy Univ Angers Polytech Angers Laris Angers France

Albeit not equivalent, in many applications the gamma and the inverse Gaussian processes are treated as if they were. This circumstance makes the misspecification problem of these models interesting and important, especially when data are affected by measurement errors, since noisy/perturbed data do not allow to verify whether the selected model is actually able to adequately fit the real (hidden) degradation process. Motivated by the above considerations, in this paper we conduct a large Monte Carlo study to evaluate whether and how the presence of measurement errors affects this misspecification issue. The study is performed considering as reference models a perturbed gamma process recently proposed in the literature and a new perturbed inverse Gaussian process that share the same non-Gaussian distributed error term. As an alternative option, we also analyze the more classical case where the error term is Gaussian distributed. We consider both the situation where the true model is the perturbed gamma and the one where it is the perturbed inverse Gaussian. Model parameters are estimated from perturbed data using the maximum likelihood method. Estimates are retrieved by using a new sequential Monte Carlo EM algorithm, which use allows to hugely mitigate the severe numerical issues posed by the direct maximization of the likelihood. The risk of incurring in a misspecification is evaluated as percentage of times the Akaike information criterion leads to select the wrong model. The severity of a misspecification is evaluated in terms of its impact on maximum likelihood estimate of the mean remaining useful life.

关键词： expectation-maximization algorithm gamma process inverse Gaussian process measurement errors model misspecification particle filter

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Explaining stock return distributions via an agent-based model

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NONLINEAR DYNAMICS 2021年第1期105卷 1063-1096页

作者： Seedat, Shaheen Abelman, Shirley Univ Witwatersrand Sch Comp Sci & Appl Math Private Bag 3 ZA-2050 Johannesburg South Africa

US stock returns exhibit mixed Gaussian probabilistic features as well as nonlinear and complex dynamics, but existing agent-based models of stock markets have not focused on replicating or explaining all these phenomena jointly. In this paper, a new agent-based model of the stock market is proposed that can replicate and explain such phenomena jointly. In the new model, stocks are a claim to a dividend process determined by a hidden state process which follows a Markov chain. The model produces a probability distribution of stock returns that is mixed Gaussian, like the US stock market. Using a generalized multiscale entropy method, it is shown that the simulated returns have similar complexity and entropy properties to US stock returns for plausible parameter values. Sensitivity analyses show that the simulated stock price series generated by the model varies in a plausible manner with various underlying important parameters such as agent risk aversion, agent beliefs, the underlying stock dividend process, returns to risk-free assets and dividend transition probabilities.

关键词： Complexity analysis Multiscale entropy Agent-based modelling Mixed Gaussian models Stock return distributions expectation-maximization algorithm

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