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A discrete-time and finite-state Markov Chain model for association football matches

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2018年第8期47卷 2476-2485页

作者： Zou, Qingrong Li, Qi Guo, Hao Shi, Jian Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Jilin Univ Editorial Dept Journal Changchun Jilin Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China

A birth process model proposed by Dixon and Robinson has been widely used in football spread betting market. However, multiple goals in a minute are permitted in the model, which does not conform to historical record. Moreover, it is difficult to calculate the outcome probability of the process accurately. The article presents a discrete-time and finite-state Markov chain model for real-time forecast of football matches and a recursive algorithm is derived to calculate the outcome probability accurately. The empirical study shows that the proposed model outperforms the models of Dixon and Robinson and Dixon and Coles.

关键词： Association football Discrete-time and finite-state Markov chain Football outcome forecast Maximum likelihood recursive algorithm 65C20 62M20 60J10 62P25

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A Single-Pass algorithm for Spectrum Estimation With Fast Convergence

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IEEE TRANSACTIONS ON INFORMATION THEORY 2011年第7期57卷 4720-4731页

作者： Xiao, Han Wu, Wei Biao Univ Chicago Dept Stat Chicago IL 60637 USA

We propose a single-pass algorithm for estimating spectral densities of stationary processes. Our algorithm is computationally fast in the sense that, when a new observation arrives, it can provide a real-time update within O (1) computation. The proposed algorithm is probabilistically fast in that, for stationary processes whose auto-covariances decay geometrically, the estimates from the algorithm converge at a rate which is optimal up to a multiplicative logarithmic factor. We also establish asymptotic normality for the recursive estimate. A simulation study is carried out and it confirms the superiority over the classical batched mean estimates.

关键词： Batched mean estimate bias reduction nonparametric estimation physical dependence measure recursive algorithm spectral density stochastic process

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Subspace-based prediction of linear time-varying stochastic systems

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AUTOMATICA 2007年第12期43卷 2009-2021页

作者： Kameyama, Kentaro Ohsumi, Akira Fukui Natl Coll Technol Dept Mech Engn Fukui 9168507 Japan Kyoto Inst Technol Grad Sch Sci & Technol Dept Mech & Syst Engn Kyoto 6068585 Japan

In this paper, a new subspace method for predicting time-varying stochastic systems is proposed. Using the concept of angle between past and present subspaces spanned by the extended observability matrices, the future signal subspace is predicted by rotating the present subspace in the geometrical sense, and time-varying system matrices are derived from the resultant signal subspace. Proposed algorithm is improved for fast-varying systems. Furthermore, recursive implementation of both algorithms is developed. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： subspace prediction subspace methods subspace identification recursive algorithm time-varying systems

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Online updating belief rule based system for pipeline leak detection under expert intervention

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第4期36卷 7700-7709页

作者： Zhou, Zhi-Jie Hu, Chang-Hua Yang, Jian-Bo Xu, Dong-Ling Zhou, Dong-Hua Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England High Tech Inst Xian Xian 710025 Shaanxi Peoples R China

A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for pipeline leak detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the pipeline leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for pipeline leak detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and pipeline operating data, and validated by testing data. All training and testing data are collected from a real pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the in

关键词： Belief rule base Expert system Evidential reasoning recursive algorithm Leak detection

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Online Kernel Principal Component Analysis: A Reduced-Order Model

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2012年第9期34卷 1814-1826页

作者： Honeine, Paul Univ Technol Troyes Inst Charles Delaunay Lab Modelisat & Surete Syst CNRSUMR 6279 F-10010 Troyes France

Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis. In this paper, we propose an online algorithm for kernel-PCA. To this end, we examine a kernel-based version of Oja's rule, initially put forward to extract a linear principal axe. As with most kernel-based machines, the model order equals the number of available observations. To provide an online scheme, we propose to control the model order. We discuss theoretical results, such as an upper bound on the error of approximating the principal functions with the reduced-order model. We derive a recursive algorithm to discover the first principal axis, and extend it to multiple axes. Experimental results demonstrate the effectiveness of the proposed approach, both on synthetic data set and on images of handwritten digits, with comparison to classical kernel-PCA and iterative kernel-PCA.

关键词： Principal component analysis online algorithm machine learning reproducing kernel Oja's rule recursive algorithm

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A Unified BRB-Based Framework for Real-Time Health Status Prediction in High-Speed Trains

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第9期71卷 9272-9282页

作者： Cheng, Chao Guo, Yuhong Wang, Jiuhe Chen, Hongtian Shao, Junjie Changchun Univ Technol Changchun 130012 Jilin Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada CRRC Changchun Railway Vehicles Co Ltd Data Res Off Changchun 130062 Peoples R China

The health status of the running gear in high-speed trains changes dynamically with time in a complete life cycle. Running gear systems composed of many coupled components are complex, and health statuses of which are difficult to predict in real time through a traditional health status prediction scheme. Lately, belief rule base (BRB), which is able to combine quantitative information and expert knowledge, has shown excellent expression in modeling complex systems. In the procedure of health status prediction, expert expertise can sufficiently enhance the accuracy and efficiency of this model. Therefore, this paper puts forwards a real-time health status prediction framework based on a multi-layer BRB with priority scheduling strategies for running gears. In the first-layer BRB, a time-series prediction model of multiple module BRB considering complete features is established. In the second-layer model, grey relation analysis (GRA) is employed in priority scheduling strategies of features. The third-layer BRB is used for assessing the health status of running gears by combining the features. In addition, the initial parameters of all module BRB given by experts may not be precise. Accordingly, the initial parameters in the BRB are updated by the recursive algorithm online. Finally, the proposed method is tested on the testing platform in running gears. The results make it clear the proposed method can predict the health status of running gears with much accuracy in real time.

关键词： Gears Predictive models Real-time systems Mathematical models Hidden Markov models Data models Monitoring Health status prediction priority scheduling strategies belief rule base (BRB) recursive algorithm grey relation analysis (GRA)

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Simulation of industrial manipulators based on the UDU^T decomposition of inertia matrix

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MULTIBODY SYSTEM DYNAMICS 2003年第1期9卷 63-85页

作者： Saha, SK Indian Inst Technol Dept Mech Engn New Delhi 110016 India

The UDUT - U and D are respectively the upper triangular and diagonal matrices - decomposition of the generalized inertia matrix of an n-link serial manipulator, introduced elsewhere, is used here for the simulation of industrial manipulators which are mainly of serial type. The decomposition is based on the application of the Gaussian elimination rules to the recursive expressions of the elements of the inertia matrix that are obtained using the Decoupled Natural Orthogonal Complement matrices. The decomposition resulted in an efficient order n, i.e., O(n), recursive forward dynamics algorithm that calculates the joint accelerations. These accelerations are then integrated numerically to perform simulation. Using this methodology, a computer algorithm for the simulation of any n degrees of freedom (DOF) industrial manipulator comprising of revolute and/or prismatic joints is developed. As illustrations, simulation results of three manipulators, namely, a three-DOF planar manipulator, and the six-DOF Stanford arm and PUMA robot, are reported in this paper.

关键词： manipulators forward dynamics recursive algorithm simulation

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recursive Least Squares and Multi-innovation Stochastic Gradient Parameter Estimation Methods for Signal Modeling

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2017年第4期36卷 1735-1753页

作者： Xu, Ling Ding, Feng Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Wuxi Vocat Inst Commerce Sch Internet Things Technol Wuxi 214153 Peoples R China

The sine signals are widely used in signal processing, communication technology, system performance analysis and system identification. Many periodic signals can be transformed into the sum of different harmonic sine signals by using the Fourier expansion. This paper studies the parameter estimation problem for the sine combination signals and periodic signals. In order to perform the online parameter estimation, the stochastic gradient algorithm is derived according to the gradient optimization principle. On this basis, the multi-innovation stochastic gradient parameter estimation method is presented by expanding the scalar innovation into the innovation vector for the aim of improving the estimation accuracy. Moreover, in order to enhance the stabilization of the parameter estimation method, the recursive least squares algorithm is derived by means of the trigonometric function expansion. Finally, some simulation examples are provided to show and compare the performance of the proposed approaches.

关键词： Signal modeling recursive algorithm Fourier series Parameter estimation Least squares Sine signal

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The kinematics and kinetics of multi-closed-chain mechanisms in the impact and non-impact stages

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MECCANICA 2022年第10期57卷 2591-2608页

作者： Shafei, A. M. Sadeghi, Z. Shahid Bahonar Univ Kerman Dept Mech Engn Kerman Iran

The chief objective of the current research is to outline an automated technique for deriving the governing equations and also simulating the motions of the multi-closed-chain mechanisms. To this end, the behavior of a mechanical system, whose straight and rigid links are hinged together by perfect rotary joints to make an array of closed loops, will be studied in the two phases of impact and non-impact. For the non-impact phase, in which the said mechanism is only affected by the Earth's gravity, the relevant differential equations will be derived by the recursive Gibbs-Appell (G-A) methodology (which is a simple approach with fewer partial derivatives to worry about, especially when dealing with the holonomic systems);while for the impact phase, in which at least one joint of the mechanism collides with the surrounding curved wall, the algebraic equations of the system will be obtained by the Newton's kinematic impact law. One of the most formidable challenges in the current research is the presence of the holonomic constraints that govern the kinematics of the problem. The number of these constraints goes up linearly with the increase in the number of system loops. The other challenges and issues that have been successfully addressed by the presented technique include the oblique impact of the joints of the said mechanism with the surrounding curved wall and the relatively large number of the dependent generalized coordinates needed to define the system configuration. In the suggested approach, after deriving the inverse kinetic equations of the considered mechanism, they are converted to the forward dynamics form in a graphical procedure to simulate the system's dynamic responses. To demonstrate the capability and the efficacy of the presented technique in deriving the kinetic equations of multi-closed-loop systems, the dynamic behavior of a 5RRR mechanism with 4 closed loops and 10 rigid links is simulated. The simulation results are then validated by means of

关键词： Closed-loop mechanism Oblique impact Gibbs-Appell method Holonomic constraints recursive algorithm

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Selecting radial basis function network centers with recursive orthogonal least squares training

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2000年第2期11卷 306-314页

作者： Gomm, JB Yu, DL Liverpool John Moores Univ Sch Engn Control Syst Res Grp Liverpool L3 3AF Merseyside England

recursive orthogonal least squares (ROLS) is a numerically robust method for solving for the output layer weights of a radial basis function (RBF) network, and requires less computer memory than the batch alternative, In this paper, the use of ROLS is extended to selecting the centers of an RBF network, It is shown that the information available in an ROLS algorithm after network training can be used to sequentially select centers to minimize the network output error. This provides efficient methods for network reduction to achieve smaller architectures with acceptable accuracy and without retraining. Two selection methods are developed, forward and backward. The methods are illustrated in applications of RBF networks to modeling a nonlinear time series and a real multiinput-multioutput chemical process, The final network models obtained achieve acceptable accuracy with significant reductions in the number of required centers.

关键词： modeling network pruning orthogonal least squares RBF neural networks recursive algorithm structure selection

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