检索结果-南通市图书馆

A Research to the autoregressive model-Based Efficient Interpolation Algorithm

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Research to the Autoregressive Model-Based Efficient Inter...

Bayesian methods for cycle slips detection based on autoregressive model

The 2011 Fourth International Symposium on Knowledge Acquisition and modeling（KAM 2011）

作者： Hong Qiu Department of Fire Engineering The Chinese People’s Armed Police Academy

In order to improve the quality of interpolated images,this thesis proposes the concept of the autoregressive-based adaptive interpolation and the respective model.The basic idea is to construct the AR model based on the pixel structures of the image;to employ the idea of iteration, having got the initial interpolated values by applying the AR model, to consider the multiple constraints that the to-be-interpolated pixel is involved, respectively to construct multiple AR models, and to weightedly summarize their results;the process of iteration can be implemented many times, until the best result is achieved. Simulation results demonstrate that this interpolation algorithm can generate interpolated images with excellent subjective quality as well as high PSNRs.

关键词： Image interpolation autoregressive model the least square method weight iteration

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Bayesian methods for cycle slips detection based on autoregr...

Linear double autoregressive time series model and its conditional quantile inference

第三届中国卫星导航学术年会

作者： *Qianqian Zhang1, Qingming Gui1,2, Jianwen Li2, Yisong Gong3, SonghuiHan1,2(1 Institute of Science, Information Engineering University, 62 Kexue Road,Zhengzhou 450001, China)(2 Institute of Surveying and Mapping, Information Engineering University, 66 MiddleLonghai Road, Zhengzhou 450052, China)(3. 61618 Troops, Beijing, 102102)

A new approach, Bayesian method for single frequent cycle slips detection based on autoregressive model, is presented by exploiting modern Bayesian statistical theory. Besides, this paper deals with the problem of masking and swamping about cycle slips detection in a thorough new conception and gives the corresponding solution. First of all, considering the character of cycle slips in phase data and the relations between cycle slips and outliers, this paper proposes a Bayesian method for cycle slips detection based on the posterior probabilities of classification variables in the respective of Bayesian hypothesis. Secondly, an adaptive Gibbs sampling algorithm is designed through analyzing the reasons of masking and swamping about cycle slips detection. Then an unmasking Bayesian method for cycle slips detection is proposed. Thirdly, accurate estimation of cycle slips is given based on Bayesian point estimation. Finally, the method is applied to real phase data to test its correction and efficiency.

关键词： Cycle Slips Detection Masking Swamping autoregressive model Classification Variables Bayesian Method

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Linear double autoregressive time series model and its condi...

Predicting the output error of the suboptimal state estimator to improve the performance of the MPC-based artificial pancreas

第十届海峡两岸统计与概率研讨会

作者： Qianqian Zhu Yao Zheng Guodong Li University of Hong Kong

This paper proposes a new conditional heteroscedastic model, called the linear double autoregressive(AR) model. Its conditional quantile inference tools are studied without imposing any moment condition on the process or the innovations, in contrast to existing inference tools for conditional heteroscedastic models which all require that the innovations have a nite variance. The existence of strictly stationary solutions to the linear double AR model is discussed, and a necessary and su cient condition is established by borrowing the linearity of the random coe cient AR model. We introduce the doubly weighted conditional quantile estimation(CQE) for the model, where the rst set of weights ensures the asymptotic normality of the estimators, and the second improves its e ciency through balancing individual CQEs across multiple quantile levels. Finally, goodness-of- t tests based on the quantile autocorrelation function are suggested for the adequacy of tted models. Simulation studies indicate that the proposed inference tools perform well in nite samples, and an empirical example is presented to illustrate the usefulness of the new model.

关键词： autoregressive model Conditional quantile estimation Goodness-of-t test Heavy tail Stationary solution

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Control Theory and Technology 2023年第4期21卷 541-554页

作者： Martin Dodek Eva Miklovičová Institute of Robotics and Cybernetics Faculty of Electrical Engineering and Information TechnologySlovak University of Technology in BratislavaIlkovičova 3Bratislava81219Slovakia

The error of single step-ahead output prediction is the information traditionally used to correct the state estimate while exploiting the new measurement of the system output.However,its dynamics and statistical properties can be further studied and exploited in other ways.It is known that in the case of suboptimal state estimation,this output prediction error forms a correlated sequence,hence it can be effectively predicted in real time.Such a suboptimal scenario is typical in applications where the process noise model is not known or it is uncertain.Therefore,the paper deals with the problems of analytical and empirical modeling,identification,and prediction of the output error of the suboptimal state estimator for the sake of improving the output prediction accuracy and ultimately the performance of the model predictive control.The improvements are validated on an empirical model of type 1 diabetes within an in-silico experiment focused on glycemia prediction and implementation of the MPC-based artificial pancreas.

关键词： Suboptimal state estimation Prediction autoregressive model Moving average model model predictive control Diabetes mellitus Artificial pancreas

A Unit Root Test for an AR(1)Process with AR Errors by Using Random Weighted Bootstrap

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

数学学报:英文版 2023年第9期39卷 1834-1854页

作者： Xiao Hui Liu Ya Wen Fan Yu Zi Liu Shi Hua Luo School of Statistics and Data Science Jiangxi University of Finance and EconomicsNanchang330013P.R.China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and EconomicsNanchang330013P.R.China

A great deal of economic problems are related to detecting the stability of time series data,where the main interest is in the unit root test.In this paper,we consider the unit root testing problem with errors being long-memory processes with the LARCH structure.A new test statistic is developed by using the random weighted bootstrap method.It turns out that the proposed statistic has a chisquared distribution asymptotically regardless of the process being stationary or nonst at ionary,and with or without an intercept term.The simulation results show that the statistic has a desired finite sample performance in terms of both size and power.A real data application is also given relying on the inflation rate data of 17 countries.

关键词： autoregressive model random weighted bootstrap autoregressive errors unit root test

Empirical likelihood inference in autoregressive models with time-varying variances

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Statistical Theory and Related Fields 2022年第2期6卷 129-138页

作者： Yu Han Chunming Zhang College of Science Northeast Electric Power UniversityJilinPeople’s Republic of China Department of Statistics University of Wisconsin-MadisonMadisonWIUSA

This paper develops the empirical likelihood(EL)inference procedure for parameters in autore-gressive models with the error variances scaled by an unknown nonparametric time-varying function.Compared with existing methods based on non-parametric and semi-parametric esti-mation,the proposed test statistic avoids estimating the variance function,while maintaining the asymptotic chi-square distribution under the null.Simulation studies demonstrate that the proposed EL procedure(a)is more stable,i.e.,depending less on the change points in the error variances,and(b)gets closer to the desired confidence level,than the traditional test statistic.

关键词： Empirical likelihood autoregressive model unconditional heteroscedasticity stable test

A Score Type Test for General autoregressive models in Time Series

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Mathematicae Applicatae Sinica 2007年第3期23卷 439-450页

作者： Jian-hong Wu Li-xing Zhu College of Statistics and Mathematics Zhejiang Gongshang University Hangzhou 310018 China Hong Kong Baptist University Hong Kong China East China Normal University Shanghai 200062 China

This paper is devoted to the goodness-of-fit test for the general autoregressive models in time series. By averaging for the weighted residuals, we construct a score type test which is asymptotically standard chi-squared under the null and has some desirable power properties under the alternatives. Specifically, the test is sensitive to alternatives and can detect the alternatives approaching, along a direction, the null at a rate that is arbitrarily close to n-1/2. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of alternatives. The performance of the tests is evaluated through simulation studies.

关键词： autoregressive model goodness-of-fit maximin test model checking score type test time series

Time Series Analysis for Vibration-Based Structural Health Monitoring:A Review

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Structural Durability & Health Monitoring 2018年第3期12卷 129-147页

作者： Kong Fah Tee School of Engineering University of GreenwichKentUK.

Structural health monitoring(SHM)is a vast,interdisciplinary research field whose literature spans several decades with focusing on condition assessment of different types of structures including aerospace,mechanical and civil structures.The need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based inspection.Statistical time series methods for SHM form an important and rapidly evolving category within the broader vibration-based methods.In the literature on the structural damage detection,many time series-based methods have been proposed.When a considered time series model approximates the vibration response of a structure and model coefficients or residual error are obtained,any deviations in these coefficients or residual error can be inferred as an indication of a change or damage in the structure.Depending on the technique employed,various damage sensitive features have been proposed to capture the deviations.This paper reviews the application of time series analysis for SHM.The different types of time series analysis are described,and the basic principles are explained in detail.Then,the literature is reviewed based on how a damage sensitive feature is formed.In addition,some investigations that have attempted to modify and/or combine time series analysis with other approaches for better damage identification are presented.

关键词： Time series snalysis structural health monitoring structural damage detection autoregressive model damage sensitive features

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment plants

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Frontiers of Environmental Science & Engineering 2014年第1期8卷 128-136页

作者： Shuai MA Siyu ZENG Xin DONG Jining CHEN Gustaf OLSSON State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing 100084 China Department of Industrial Electrical Engineering and Automation Lurid University Lund SE-22100 Sweden

The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia （NH3） concentrations： 1） data filtering using wavelet decomposition and reconstruction; 2） typical cycle identification using power spectrum density analysis; 3） fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1） for both of the two plants, the periodicity affects the flow rate and NH3 concentrations in different cycles （especially cycles longer than 1 day）; 2） when the flow rate and NH3 concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3） the periodicity influence is larger than rainfalls; 4） the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.

关键词： influent load prediction wavelet de-noising,power spectrum density autoregressive model time-frequency analysis wastewater treatment