检索结果-南通市图书馆

River channel flood forecasting method of coupling wavelet neural network with autoregressive model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Southeast University(English Edition) 2008年第1期24卷 90-94页

作者：李致家周轶马振坤河海大学水文水资源学院南京210098

Based on analyzing the limitations of the commonly used back-propagation neural network （BPNN）, a wavelet neural network （WNN） is adopted as the nonlinear river channel flood forecasting method replacing the BPNN. The WNN has the characteristics of fast convergence and improved capability of nonlinear approximation. For the purpose of adapting the timevarying characteristics of flood routing, the WNN is coupled with an AR real-time correction model. The AR model is utilized to calculate the forecast error. The coefficients of the AR real-time correction model are dynamically updated by an adaptive fading factor recursive least square（RLS） method. The application of the flood forecasting method in the cross section of Xijiang River at Gaoyao shows its effectiveness.

关键词： river channel flood forecasting wavel'et neural network autoregressive model recursive least square( RLS) adaptive fading factor

Bootstrap Approaches to autoregressive model on Exchange Rates Currency

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

Open Journal of Statistics 2016年第6期6卷 1010-1024页

作者： Muhamad Safiih Lola Anthea David Nurul Hila Zainuddin School of Informatics and Applied Mathematics Universiti Malaysia Terengganu Kuala Nerus Malaysia

The use of historical data is important in making the predictions, for instance in the exchange rate. However, in the construction of a model, extreme data or dirtiness of data is inevitable. In this study, AR model is used with the exchange rate historical data (January 2007 until December 2007) for USD/MYR and is divided into 1-, 3- and 6-horizontal months respectively. Since the presence of extreme data will affect the accuracy of the results obtained in a prediction. Therefore, to obtain a more accurate prediction results, the bootstrap approach was implemented by hybrid with AR model coins as the Bootstrap autoregressive model (BAR). The effectiveness of the proposed model is investigated by comparing the existing and the proposed model through the statistical performance methods which are RMSE, MAE and MAD. The comparison involves 1%, 5% and 10% for each horizontal month. The results showed that the BAR model performed better than the AR model in terms of sensitivity to extreme data, the accuracy of forecasting models, efficiency and predictability of the model prediction. In conclusion, bootstrap method can alleviate the sensitivity of the model to the extreme data, thereby improving the accuracy of forecasting model which also have high prediction efficiency and that can increase the predictability of the model.

关键词： autoregressive model Outliers Bootstrap Robust

Bayesian methods for cycle slips detection based on autoregressive model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

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

A Research to the autoregressive model-Based Efficient Interpolation Algorithm

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

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

Linear double autoregressive time series model and its conditional quantile inference

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 *** 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

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

A Score Type Test for General autoregressive models in Time Series

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

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

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 *** 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 *** 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

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

在线全文

维普期刊数据库

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 *** need for quantitative global damage detection methods that can be applied to complex structures has led to vibration-based *** time series methods for SHM form an important and rapidly evolving category within the broader vibration-based *** the literature on the structural damage detection,many time series-based methods have been *** 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 *** on the technique employed,various damage sensitive features have been proposed to capture the *** paper reviews the application of time series analysis for *** different types of time series analysis are described,and the basic principles are explained in ***,the literature is reviewed based on how a damage sensitive feature is *** 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

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

在线全文

维普期刊数据库

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 ***,its dynamics and statistical properties can be further studied and exploited in other *** 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 *** a suboptimal scenario is typical in applications where the process noise model is not known or it is ***,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 *** 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