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The Fuzzy Modeling algorithm for Complex Systems Based on Stochastic Neural Network

Journal of Systems Engineering and Electronics 2002年第3期13卷 46-51页

作者：李波张世英李银惠 School of Management Tianjin University Tianjin 300072 P. R. China

A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.

关键词： Complex system modeling General stochastic neural network MTS fuzzy model expectation-maximization algorithm

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Statistical Learning in Drug Discovery via Clustering and Mixtures

Statistical Learning in Drug Discovery via Clustering and Mi...

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作者： Wang, Xu University of Waterloo

学位级别：master

In drug discovery, thousands of compounds are assayed to detect activity against abiological target. The goal of drug discovery is to identify compounds that are active against the target (e.g. inhibit a virus). Statistical learning in drug discovery seeks to build a model that uses descriptors characterizing molecular structure to predict biological activity. However, the characteristics of drug discovery data can make it difficult to model the relationship between molecular descriptors and biological activity. Among these characteristics are the rarity of active compounds, the largevolume of compounds tested by high-throughput screening, and the complexity ofmolecular structure and its relationship to *** thesis focuses on the design of statistical learning algorithms/models andtheir applications to drug discovery. The two main parts of the thesis are: analgorithm-based statistical method and a more formal model-based approach. Bothapproaches can facilitate and accelerate the process of developing new drugs. Aunifying theme is the use of unsupervised methods as components of supervisedlearning algorithms/*** the first part of the thesis, we explore a sequential screening approach, ClusterStructure-Activity Relationship Analysis (CSARA). Sequential screening integratesHigh Throughput Screening with mathematical modeling to sequentially select thebest compounds. CSARA is a cluster-based and algorithm driven method. Togain further insight into this method, we use three carefully designed experimentsto compare predictive accuracy with Recursive Partitioning, a popular structureactivityrelationship analysis method. The experiments show that CSARA outperformsRecursive Partitioning. Comparisons include problems with many descriptorsets and situations in which many descriptors are not important for *** the second part of the thesis, we propose and develop constrained mixturediscriminant analysis (CMDA), a model-based method. The main idea of CMDAis

关键词： Drug Discovery Cluster Structure-Activity Relationship Analysis Constrained Mixture Discriminant Analysis expectation-maximization algorithm

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Efficient Learning of Continuous-Time Hidden Markov Models with Discrete-Time Irregular Observations for Healthcare Intervention Planning

Efficient Learning of Continuous-Time Hidden Markov Models w...

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作者： Ghodsi, Saeed University of California Los Angeles

学位级别：M.S., Master of Science/Master of Surgery

The availability of vast amounts of electronic medical records data has inspired an increasing interest in data-driven healthcare intervention planning methods. Disease progression models provide a mechanism for understanding and predicting the impact of interventions on the health state of patients. Most traditional Markovian state-transition models perform poorly on real-world data since they are incapable of capturing complexities such as unobservability of the underlying health state and irregularity of visit times. Moreover, most of the existing frameworks are unable to explicitly model the effect of interventions on disease progression. CT-HMMs have recently attracted attention, as they are able to handle these complexities. Our main contribution is to propose a CT-HMM disease progression model, which incorporates the effect of interventions, and to present an efficient approach for learning the parameters of this model based on the EM algorithm. We demonstrate the effectiveness of our algorithm by performing experiments on synthetic data.

关键词： Disease progression modeling expectation-maximization algorithm Healthcare intervention Planning Hidden Markov model

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Quantifying the age peaks,age ranges and weights of detrital ages based on the EM algorithm

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Geoscience Frontiers 2024年第4期15卷 217-229页

作者： Jintao Kong Linfin branch of PetroChina Coalbed Methane Company Limited Linfen 041000Shanxi ProvinceChina PetroChina Coalbed Methane Company Limited Beijing 100028China College of Earth Sciences Jilin UniversityChangchun 130061Jilin ProvinceChina

Detrital geochronology fundamentally involves the quantification of major age ranges and their weights winthin an age *** study presents a streamlined approach,modeling the age distribution of detrital zircons using a normal mixture model,and employs the expectation-maximization(EM)algorithm for precise estimations.A method is introduced to automatically select appropriate initial mean values for EM algorithm,enhancing its efficacy in detrital *** process entails multiple trials with varying numbers of age components leading to diverse k-component *** model with the lowest Bayesian Information Criterion(BIC)is identified as the most *** accurate component number and weight determination,a substantial sample size(n>200)is advisable. Our findings based on both synthetic and empirical datasets confirm that the normal mixture model,refined by the EM algorithm,reliably identifies key age parameters with minimal *** a kind of probability density estimator,the normal mixture model offers a novel visualization tool for detrital data and an alternative foundation for KDE in calculating existing similarity *** focus of this study is the critical examination of quantitative metrics for comparing detrital zircon age *** a case study,this study demonstrates that metrics based on empirical cumulative probability distribution(such as K-S and Kuiper statistics)may lead to erroneous *** employment of the Kullback-Leibler(KL)divergence,a metric grounded in probability density estimation,is *** critical values,simulated via the Monte Carlo method,provide more objective benchmarks for these quantitative metrics. All methodologies discussed are encapsulated in a series of MATLAB scripts,available as open-source code and a standalone application,facilitating wider adoption and application in the field.

关键词： Detrital zircongeochronology expectation-maximization algorithm Kullback-Leibler divergence Quantifying comparison Age peaks,ageranges,andweights

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A Fixed-Threshold Method for Estimating Fractional Vegetation Cover of Maize under Different Levels of Water Stress

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REMOTE SENSING 2021年第5期13卷 1009-1009页

作者： Niu, Yaxiao Zhang, Huihui Han, Wenting Zhang, Liyuan Chen, Haipeng Northwest A&F Univ Coll Mech & Elect Engn Xianyang 712100 Peoples R China USDA ARS Water Management & Syst Res Unit 2150 Ctr AveBldg D Ft Collins CO 80526 USA Minist Agr Key Lab Agr Internet Things Xianyang 712100 Peoples R China Colorado State Univ Dept Civil & Environm Engn Ft Collins CO 80523 USA

Accurate estimation of fractional vegetation cover (FVC) from digital images taken by commercially available cameras is of great significance in order to monitor the vegetation growth status, especially when plants are under water stress. Two classic threshold-based methods, namely, the intersection method (T-1 method) and the equal misclassification probability method (T-2 method), have been widely applied to Red-Green-Blue (RGB) images. However, the high coverage and severe water stress of crops in the field make it difficult to extract FVC stably and accurately. To solve this problem, this paper proposes a fixed-threshold method based on the statistical analysis of thresholds obtained from the two classic threshold approaches. Firstly, a Gaussian mixture model (GMM), including the distributions of green vegetation and backgrounds, was fitted on four color features: excessive green index, H channel of the Hue-Saturation-Value (HSV) color space, a* channel of the CIE L*a*b* color space, and the brightness-enhanced a* channel (denoted as a*_I). Secondly, thresholds were calculated by applying the T-1 and T-2 methods to the GMM of each color feature. Thirdly, based on the statistical analysis of the thresholds with better performance between T-1 and T-2, the fixed-threshold method was proposed. Finally, the fixed-threshold method was applied to the optimal color feature a*_I to estimate FVC, and was compared with the two classic approaches. Results showed that, for some images with high reference FVC, FVC was seriously underestimated by 0.128 and 0.141 when using the T-1 and T-2 methods, respectively, but this problem was eliminated by the proposed fixed-threshold method. Compared with the T-1 and T-2 methods, for images taken in plots under severe water stress, the mean absolute error of FVC obtained by the fixed-threshold method was decreased by 0.043 and 0.193, respectively. Overall, the FVC estimation using the proposed fixed-threshold method has the advantages o

关键词： proximal RGB image color feature Gaussian mixture model expectation-maximization algorithm

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Estimating Conditional Probabilities for the Detection of Unfavorable Copy Number Alterations in a Targeted Therapy

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第10期60卷 2933-2942页

作者： Hsu, Fang-Han Dougherty, Edward R. Chen, Yidong Serpedin, Erchin Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Translat Genom Res Inst TGEN Phoenix AZ 85004 USA Univ Texas Hlth Sci Ctr San Antonio Dept Epidemiol & Biostat San Antonio TX 78229 USA Univ Texas Hlth Sci Ctr San Antonio Greehey Childrens Canc Res Inst San Antonio TX 78229 USA

Emerging targeted therapies have shown benefits such as less toxicity and higher effectiveness in specific types of cancer treatment;however, the accessibility of these advantages may rely on correct identification of suitable patients, which remains highly immature. We assume that copy number profiles, being accessible genomic data via microarray techniques, can provide useful information regarding drug response and shed light on personalized therapy. Based on the mechanism of action (MOA) of trastuzumab in the HER2 signaling pathway, a Bayesian network model in which copy number alterations (CNAs) serve as latent parents modifying signal transduction is applied. Two model parameters M-score and R-value which stand for the qualitative and quantitative effects of CNAs on drug effectiveness and are functions of conditional probabilities (CPs), are defined. An expectation-maximization (EM) algorithm is developed for estimating CPs, M-scores, and R-values from continuous measures, such as microarray data. We show through simulations that the EM algorithm can outperform classical threshold-based methods in the estimation of CPs and thereby provide improved performance for the detection of unfavorable CNAs. Several candidates of unfavorable CNAs to the trastuzumab therapy in breast cancer are provided in a real data example.

关键词： Bayesian network copy number drug response expectation-maximization algorithm gene expression

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An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

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Petroleum Exploration and Development 2022年第2期49卷 339-348页

作者： GE Xinmin XUE Zong’an ZHOU Jun HU Falong LI Jiangtao ZHANG Hengrong WANG Shuolong NIU Shenyuan ZHAO Ji’er School of Geosciences China University of PetroleumQingdao 266580China CNPC Key Well Logging Laboratory Xi’an 710077China Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071China Oil and Gas Survey Center of China Geological Survey Beijing 100083China China Petroleum Logging Co.Ltd. Xi’an 710077China PetroChina Research Institute of Petroleum Exploration&Development Beijing 100083China PetroChina Qinghai Oilfield Company Dunhuang 736202China Zhanjiang Branch of CNOOC Ltd. Zhanjiang 524057China Sinopec Research Institute of Petroleum Engineering Beijing 100101China

To make the quantitative results of nuclear magnetic resonance(NMR) transverse relaxation(T;) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T;spectrums based on the Gaussian mixture model(GMM). Firstly, We conducted the principal component analysis on T;spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T;spectrum features and pore structure types of different clustering groups were analyzed and compared with T;geometric mean and T;arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T;spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

关键词： NMR T2 spectrum Gaussian mixture model expectation-maximization algorithm Akaike information criterion unsupervised clustering method quantitative pore structure evaluation

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New Concept of PLC Modems: Multi-Carrier System for Frequency Selective Slow-Fading Channels Based on Layered SCCC Turbocodes

New Concept of PLC Modems: Multi-Carrier System for Frequenc...

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作者： Zavrtalek, J. Brno University of Technology

The article introduces a novel concept of a PLC modem as a complement to the existing G3 and PRIME standards for communications using medium- or high-voltage overhead or cable lines. The proposed concept is based on the fact that the levels of impulse noise and frequency selectivity are lower on high-voltage lines than on low-voltage ones. Also, the demands for “cost-effective” circuitry design are not so crucial as in the case of modems for low-voltage level. In contract to these positive conditions, however, there is the need to overcome much longer distances and to take into account low SNR on the receiving side. With respect to the listed reasons, our concept makes use of MCM, instead of OFDM. The assumption of low SNR is compensated through the use of an efficient channel coding based on a serially concatenated turbo code. In addition, MCM offers lower latency and PAPR compared to OFDM. Therefore, when using MCM, it is possible to excite the line with higher power. The proposed concept has been verified during experimental transmission of testing data over a real, 5 km long, 22kV overhead line.

关键词： Power line communication multi-carrier modulation serially concatenated convolutional codes Iterative detection BCJR forward-backward algorithm soft-output Viterbi algorithm soft-in soft-out module expectation-maximization algorithm soft decision

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Using of Hand Geometry in Biometric Security Systems

Using of Hand Geometry in Biometric Security Systems

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作者： Varchol, P. Brno University of Technology

In this paper, biometric security system for access control based on hand geometry is presented. Biometric technologies are becoming the foundation of an extensive array of highly secure identification and personal verification solutions. Experiments show that the physical dimensions of a human hand contain information that is capable to verify the identity of an individual. The database created for our system consists of 408 hand images from 24 people of young ages and different sex. Different pattern recognition techniques have been tested to be used for verification. Achieved experimental results FAR=0,1812% and FRR=14,583% show the possibilities of using this system in environment with medium security level with full acceptance from all users.

关键词： Biometric security hand geometry recognition Gaussian mixture model expectation-maximization algorithm

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Robust low-rank covariance matrix estimation with a general pattern of missing values

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SIGNAL PROCESSING 2022年 195卷 108460-108460页

作者： Hippert-Ferrer, A. El Korso, M. N. Breloy, A. Ginolhac, G. Univ Paris Saclay Lab Signaux & Syst Cent Suplec CNRS F-91190 Gif Sur Yvette France Paris Nanterre Univ LEME 50 Rue Sevres F-92410 Ville Davray France Savoie Mt Blanc Univ LISTIC 5 Chemin Bellevue Annecy France

A B S T R A C T This paper tackles the problem of robust covariance matrix estimation when the data is incomplete. Classical statistical estimation methodologies are usually built upon the Gaussian assumption, whereas existing robust estimation ones assume unstructured signal models. The former can be inaccurate in real world data sets in which heterogeneity causes heavy-tail distributions, while the latter does not profit from the usual low-rank structure of the signal. Taking advantage of both worlds, a covariance matrix estimation procedure is designed on a robust (mixture of scaled Gaussian) low-rank model by leveraging the observed-data likelihood function within an expectation-maximization algorithm. It is also designed to handle general pattern of missing values. The proposed procedure is first validated on simulated data sets. Then, its interest for classification and clustering applications is assessed on two real data sets with missing values, which include multispectral and hyperspectral time series.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Missing data Covariance matrix Mixture of scaled Gaussian Low-rank expectation-maximization algorithm Classification

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