Inference for Weibull distribution under generalized order statistics

Based on generalized order statistics from Weibull distribution the approach of Bayesian and non-Bayesian estimation are discussed. We present a simple and efficient simulational algorithm for generating a generalized order statistics sample from any continuous distribution. Specializations to Bayesian and non-Bayesian estimators, some lifetime parameters and confidence intervals of progressive II censoring and record values are obtained and compared with the existing results. Two examples are given to illustrate the proposed estimators and the simulation algorithm.     

Chattering reduction of sliding mode control by low‐pass filtering the control signal

The conventional approach to reducing control signal chattering in sliding mode control is to use the boundary layer design. However, when there is high‐level measurement noise, the boundary layer design becomes ineffective in chattering reduction. This paper, therefore, proposes a new design for chattering reduction by low‐pass filtering the control signal. The new design is non‐trivial since it requires estimation of the sliding variable via a disturbance estimator. The new sliding mode control has the same performance as the boundary layer design in noise‐free environments, and outperforms the boundary layer design in noisy environments. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于信息势能的鲁棒估计器及其在同时定位与地图构建问题中的应用

提出了一种基于信息势能鲁棒估计器来解决机器人室内的同时定位与地图构建（SLAM）问题．结构化的室内环境可以用线段近似表示．然而动态环境中,测距传感器测得的数据通常湮没在大量的噪声信号中．本文采用“分割与合并”（split—and．merge）方法进行线段的分类,根据信息势能的性能指标衡量每个采样数据对该线段的信息贡献量．按照信息优化理论设计估计器,选择信息量贡献大的样本点作为信息内点提取线段参数,构建局部地图．采用粒子滤波器进行地图及机器人路径的更新．采用递推的方法估计信息势能,降低了对样本点的信息量贡献做估计时的复杂度．仿真和实验结果证明,本文所提出的方法具有较强的鲁棒性,提高了SLAM策略的准确性和实时性．     

Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

An extension of an over-dispersion test for count data

While over-dispersion in capture-recapture studies is well known to lead to poor estimation of population size, current diagnostic tools to detect the presence of heterogeneity have not been specifically developed for capture-recapture studies. To address this, a simple and efficient method of testing for over-dispersion in zero-truncated count data is developed and evaluated. The proposed method generalizes an over-dispersion test previously suggested for un-truncated count data and may also be used for testing residual over-dispersion in zero-inflation data. Simulations suggest that the asymptotic distribution of the test statistic is standard normal and that this approximation is also reasonable for small sample sizes. The method is also shown to be more efficient than an existing test for over-dispersion adapted for the capture-recapture setting. Studies with zero-truncated and zero-inflated count data are used to illustrate the test procedures.     

基于稀疏Parzen窗密度估计的快速自适应相似度聚类方法

相似度聚类方法(Similarity-based clustering method,SCM)因其简单易实现和具有鲁棒性而广受关注.但由于内含相似度聚类算法(Similarity clustering algorithm,SCA)的高时间复杂度和凝聚型层次聚类(Agglomerative hierarchicalclu...     

Patterns for multigrid equidistributed functions: Application to general parabolas and length estimation

We show that for some special functions (called k-multigrid equidistributed functions), we can compute the limit of the frequency of patterns in the discretization of their graph, when the resolution tends to zero. This result is applied to parabolas. We deduce also that local length estimators almost never converge to the length for the parabolas.     

An enhanced error estimator on the constitutive relation for plasticity problems

This paper presents an extension to plasticity problems of a new error estimator on the constitutive relation which was introduced by P. Ladevèze in a recent paper. Numerical experiments show that this enhanced error estimator can lead to a significant improvement in the effectivity indexes in the case of anisotropic meshes, or when perfect plasticity is approached.     

Improving the estimation of Kendall's tau when censoring affects only one of the variables

This paper considers the estimation of Kendall's tau for bivariate data (X,Y) when only Y is subject to right-censoring. Although τ is estimable under weak regularity conditions, the estimators proposed by Brown et al. [1974. Nonparametric tests of independence for censored data, with applications to heart transplant studies. Reliability and Biometry, 327-354], Weier and Basu [1980. An investigation of Kendall's τ modified for censored data with applications. J. Statist. Plann. Inference 4, 381-390] and Oakes [1982. A concordance test for independence in the presence of censoring. Biometrics 38, 451-455], which are standard in this context, fail to be consistent when τ≠0 because they only use information from the marginal distributions. An exception is the renormalized estimator of Oakes [2006. On consistency of Kendall's tau under censoring. Technical Report, Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY], whose consistency has been established for all possible values of τ, but only in the context of the gamma frailty model. Wang and Wells [2000. Estimation of Kendall's tau under censoring. Statist. Sinica 10, 1199-1215] were the first to propose an estimator which accounts for joint information. Four more are developed here: the first three extend the methods of Brown et al. [1974. Nonparametric tests of independence for censored data, with applications to heart transplant studies. Reliability and Biometry, 327-354], Weier and Basu [1980, An investigation of Kendall's τ modified for censored data with applications. J. Statist. Plann. Inference 4, 381-390] and Oakes [1982, A concordance test for independence in the presence of censoring. Biometrics 38, 451-455] to account for information provided by X, while the fourth estimator inverts an estimation of Pr(Y_i?y|X_i=x_i,Y_i>c_i) to get an imputation of the value of Y_i censored at C_i=c_i. Following Lim [2006. Permutation procedures with censored data. Comput. Statist. Data Anal. 50, 332-345], a nonparametric estimator is also considered which averages the obtained from a large number of possible configurations of the observed data (X₁,Z₁),…,(X_n,Z_n), where Z_i=min(Y_i,C_i). Simulations are presented which compare these various estimators of Kendall's tau. An illustration involving the well-known Stanford heart transplant data is also presented.     

改进的选择神经网络结构的方法

有关人工神经网络作为在线状态估计器组成基于神经网络的传感器故障检测的研究已有报导。但是这些文章中没有提到如何选择神经网络的结构。神经网络的输入延迟数和隐基单元数影响其对系统的拟合精度,从而影响故障检测的灵敏度。研究了一些现有的选择神经网络结构的方法,以系统化的交叉证实法为基础,经过改进,提出了针对作为在线状态估计器的神经网络选择输入延迟数和隐层单元数的系统化的方法,并用某船在试验场中航行时平台罗经输出的一段数据作了仿真,结果证明该法可行,具有工程实用意义。