基于ANFIS控制的交流变频调速系统研究

针对交流电动机矢量控制系统因电机参数变化和负载波动等因素而使性能变差的问题,设计了一种基于自适应神经网络的模糊推理系统作为交流电动机矢量控制系统的速度调节器。仿真实验结果表明,具有自适应神经网络的模糊推理系统控制的异步电机矢量控制系统不仅动态和稳态性能都得到提高,而且具有较强的鲁棒性。     

多输出组合逻辑电路的简化设计

大多数的组合逻辑电路属于多输出电路。在进行多输出电路的设计时，由于输出变量增多，情况较单输出电路复杂，本文以具体的实例介绍了三种多输出组合电路的简化设计方法。     

Exploring the Latent Structures of Psychological Constructs in Social Development Using the Dimensional–Categorical Spectrum

This paper provides an introduction to a recently developed conceptual framework—the dimensional–categorical spectrum—for utilizing general factor mixture models to explore the latent structures of psychological constructs. This framework offers advantages over traditional latent variable models that usually employ either continuous latent factors or categorical latent class variables to characterize the latent structure and require an a priori assumption about the underlying nature of the construct as either purely dimension or purely categorical. The modeling process is discussed in detail and then illustrated with data on the delinquency items of Achenbach's child behavior checklist from a sample of children in the National Adolescent and Child Treatment Study.     

Credibility-based rescheduling model in a double-track railway network: a fuzzy reliable optimization approach

Using a space–time network to represent the choice of train trajectories, this paper proposes a fuzzy optimization framework to reschedule trains in a double-track railway network when the capacity reduction is caused by a low-probability incident. We explicitly introduce a fuzzy variable-based recovery time to capture the uncertainty of incident duration based on professional judgements or empirical estimates. The problem is then formulated as a credibilistic two-stage fuzzy 0–1 integer optimization model to find a reliable operational plan for emergency response guidance. The first stage of the model aims to generate a traversing order on the incident link, and meanwhile the second stage adaptively generates optimized schedules to evaluate the operational plan. Crisp equivalents of mathematical models are further investigated to simplify solution methodologies. The numerical experiments, which are solved by using the GAMS optimization software, demonstrate the effectiveness and efficiency of the proposed approaches.     

Some sampling properties of the two-stage test in a linear regression with a proxy variable

In thia paper, we examine the sampling properties of the two-stage test which consists of a pre-test for disturbance variance followed by a main-test a regression coefficient of an unobservable variable when a proxy variable is used instead of the unobservable variable. It is shown that the pre-test works effectively if the significance level of the pre-test is appropriately chosen.     

Robust variable selection for the varying coefficient model based on composite L 1–L 2 regression

The varying coefficient model (VCM) is an important generalization of the linear regression model and many existing estimation procedures for VCM were built on L ₂ loss, which is popular for its mathematical beauty but is not robust to non-normal errors and outliers. In this paper, we address the problem of both robustness and efficiency of estimation and variable selection procedure based on the convex combined loss of L ₁ and L ₂ instead of only quadratic loss for VCM. By using local linear modeling method, the asymptotic normality of estimation is driven and a useful selection method is proposed for the weight of composite L ₁ and L ₂. Then the variable selection procedure is given by combining local kernel smoothing with adaptive group LASSO. With appropriate selection of tuning parameters by Bayesian information criterion (BIC) the theoretical properties of the new procedure, including consistency in variable selection and the oracle property in estimation, are established. The finite sample performance of the new method is investigated through simulation studies and the analysis of body fat data. Numerical studies show that the new method is better than or at least as well as the least square-based method in terms of both robustness and efficiency for variable selection.     

人大监督权的集体行使与人大代表的个体参与

集体行使权力是人大监督权的重要特征。作为一种集体行使的权力,其运作效能取决于集体成员积极而富有意义的参与。而且,由于作为权力主体的各级人大及其常委会是人民选举产生的权力机构,其责任承担只能由选民通过选举或罢免个体的人大代表来实现。因此作为权力的参与者以及责任的承担者,个体人大代表的监督能动性对于监督权的有效运转至关重要。必须科学合理地设计人大监督权的保障、激励与约束机制,以激励代表积极参与监督权的行使,防止在集体行动中的消极不作为。     

A note on expressing wilks’ a as the product of independent beta variables

The purpose of this note is to show that the null distribution of Wilks’ A can be expressed as the product of p independent beta variables regardless of the relationship of p and q, where p is the number of variables and q is the degrees of freedom associated with the hypothesis.     

Variable Selection for Panel Count Data via Non-Concave Penalized Estimating Function

Abstract.  Variable selection is an important issue in all regression analyses, and in this paper we discuss this in the context of regression analysis of panel count data. Panel count data often occur in long-term studies that concern occurrence rate of a recurrent event, and their analysis has recently attracted a great deal of attention. However, there does not seem to exist any established approach for variable selection with respect to panel count data. For the problem, we adopt the idea behind the non-concave penalized likelihood approach and develop a non-concave penalized estimating function approach. The proposed methodology selects variables and estimates regression coefficients simultaneously, and an algorithm is presented for this process. We show that the proposed procedure performs as well as the oracle procedure in that it yields the estimates as if the correct submodel were known. Simulation studies are conducted for assessing the performance of the proposed approach and suggest that it works well for practical situations. An illustrative example from a cancer study is provided.     

Double-sampling control charts for attributes

In this article, we propose a double-sampling (DS) np control chart. We assume that the time interval between samples is fixed. The choice of the design parameters of the proposed chart and also comparisons between charts are based on statistical properties, such as the average number of samples until a signal. The optimal design parameters of the proposed control chart are obtained. During the optimization procedure, constraints are imposed on the in-control average sample size and on the in-control average run length. In this way, required statistical properties can be assured. Varying some input parameters, the proposed DS np chart is compared with the single-sampling np chart, variable sample size np chart, CUSUM np and EWMA np charts. The comparisons are carried out considering the optimal design for each chart. For the ranges of parameters considered, the DS scheme is the fastest one for the detection of increases of 100% or more in the fraction non-conforming and, moreover, the DS np chart is easy to operate.