Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter wit...Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.展开更多
The authors propose a V_(N,p) test statistic for testing finite-order serial correlation in asemiparametric varying coefficient partially linear errors-in-variables model.The test statistic is shownto have asymptotic ...The authors propose a V_(N,p) test statistic for testing finite-order serial correlation in asemiparametric varying coefficient partially linear errors-in-variables model.The test statistic is shownto have asymptotic normal distribution under the null hypothesis of no serial correlation.Some MonteCarlo experiments are conducted to examine the finite sample performance of the proposed V_(N,p) teststatistic.Simulation results confirm that the proposed test performs satisfactorily in estimated sizeand power.展开更多
Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is con...Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is contained in R are fixed design points, β =(β_1,β_2,···,β_p)′ is an unknown parameter vector, g(·) is an unknown bounded real-valuedfunction defined on a compact subset T of the real line R, and ε_k is a linear process given byε_k = ∑ from j=0 to ∞ of ψ_je_(k-j), ψ_0=1, where ∑ from j=0 to ∞ of |ψ_j| < ∞, and e_j,j=0, +-1, +-2,···, ard i.i.d. random variables. In this paper we establish the asymptoticnormality of the least squares estimator of β, a smooth estimator of g(·), and estimators of theautocovariance and autocorrelation functions of the linear process ε_k.展开更多
Resonance may occur when the periods of incoming waves are close to the eigen-periods of harbor basin.The amplified waves by resonance in harbor will induce serious wave hazards to harbor structures and vehicles in it...Resonance may occur when the periods of incoming waves are close to the eigen-periods of harbor basin.The amplified waves by resonance in harbor will induce serious wave hazards to harbor structures and vehicles in it.Through traditional theoretical approaches,the eigen-periods of harbor basin with regular shapes can be obtained.In our study,we proposed a numerical model to simulate the behavior characteristics of the harbor waves.A finite difference numerical model based on the shallow water equations(SWE) is developed to simulate incoming tsunami and tidal waves.By analyzing the time series data of water surface wave amplitude variations at selected synthetic observation locations,we estimate the wave height and arrival time in coastal area.Furthermore,we use frequency spectrum analysis to investigate the natural frequencies from the data recorded at the synthetic observation stations.展开更多
基金Project(20090162120084)supported by Research Fund for the Doctoral Program of Higher Education of ChinaProject(08JJ4014)supported by the Natural Science Foundation of Hunan Province,China
文摘Combining mathematical morphology (MM),nonparametric and nonlinear model,a novel approach for predicting slope displacement was developed to improve the prediction accuracy.A parallel-composed morphological filter with multiple structure elements was designed to process measured displacement time series with adaptive multi-scale decoupling.Whereafter,functional-coefficient auto regressive (FAR) models were established for the random subsequences.Meanwhile,the trend subsequence was processed by least squares support vector machine (LSSVM) algorithm.Finally,extrapolation results obtained were superposed to get the ultimate prediction result.Case study and comparative analysis demonstrate that the presented method can optimize training samples and show a good nonlinear predicting performance with low risk of choosing wrong algorithms.Mean absolute percentage error (MAPE) and root mean square error (RMSE) of the MM-FAR&LSSVM predicting results are as low as 1.670% and 0.172 mm,respectively,which means that the prediction accuracy are improved significantly.
基金supported by the National Natural Science Foundation of China under Grant Nos. 10871217 and 40574003the Science and Technology Project of Chongqing Education Committee under Grant No. KJ080609+1 种基金the Doctor's Start-up Research Fund under Grant No. 08-52204the Youth Science Research Fund of Chongging Technology and Business University under Grant No. 0852008
文摘The authors propose a V_(N,p) test statistic for testing finite-order serial correlation in asemiparametric varying coefficient partially linear errors-in-variables model.The test statistic is shownto have asymptotic normal distribution under the null hypothesis of no serial correlation.Some MonteCarlo experiments are conducted to examine the finite sample performance of the proposed V_(N,p) teststatistic.Simulation results confirm that the proposed test performs satisfactorily in estimated sizeand power.
基金CHEN Min's work is supported by Grant No. 70221001 and No. 70331001 from NNSFC and Grant No. KZCX2-SW-118 from CAS.
文摘Consider a semiparametric regression model with linear time series errors Y_k= x′ _kβ + g(t_k) + ε_k, 1 ≤ k ≤ n, where Y_k's are responses, x_k =(x_(k1),x_(k2),···,x_(kp))′ and t_k ∈ T is contained in R are fixed design points, β =(β_1,β_2,···,β_p)′ is an unknown parameter vector, g(·) is an unknown bounded real-valuedfunction defined on a compact subset T of the real line R, and ε_k is a linear process given byε_k = ∑ from j=0 to ∞ of ψ_je_(k-j), ψ_0=1, where ∑ from j=0 to ∞ of |ψ_j| < ∞, and e_j,j=0, +-1, +-2,···, ard i.i.d. random variables. In this paper we establish the asymptoticnormality of the least squares estimator of β, a smooth estimator of g(·), and estimators of theautocovariance and autocorrelation functions of the linear process ε_k.
基金supported by the National Natural Science Foundation of China (Grant Nos.40574012 and 40676039)National Basic Research Program of China(Grant No. 2008CB425701)+1 种基金National High-tech R& D Program of China(Grant No. 2010AA012402)K. C. Wong Magna Fund in Ningbo University
文摘Resonance may occur when the periods of incoming waves are close to the eigen-periods of harbor basin.The amplified waves by resonance in harbor will induce serious wave hazards to harbor structures and vehicles in it.Through traditional theoretical approaches,the eigen-periods of harbor basin with regular shapes can be obtained.In our study,we proposed a numerical model to simulate the behavior characteristics of the harbor waves.A finite difference numerical model based on the shallow water equations(SWE) is developed to simulate incoming tsunami and tidal waves.By analyzing the time series data of water surface wave amplitude variations at selected synthetic observation locations,we estimate the wave height and arrival time in coastal area.Furthermore,we use frequency spectrum analysis to investigate the natural frequencies from the data recorded at the synthetic observation stations.