Comparing Squared Multiple Correlation Coefficients Using Structural Equation Modeling

In social science research, a common topic in multiple regression analysis is to compare the squared multiple correlation coefficients in different populations. Existing methods based on asymptotic theories (Olkin & Finn, 1995) and bootstrapping (Chan, 2009) are available but these can only handle a 2-group comparison. Another method based on structural equation modeling (SEM) has been proposed recently. However, this method has three disadvantages. First, it requires the user to explicitly specify the sample R² as a function in terms of the basic SEM model parameters, which is sometimes troublesome and error prone. Second, it requires the specification of nonlinear constraints, which is not available in some popular SEM software programs. Third, it is for a 2-group comparison primarily. In this article, a 2-stage SEM method is proposed as an alternative. Unlike all other existing methods, the proposed method is simple to use, and it does not require any specific programming features such as the specification of nonlinear constraints. More important, the method allows a simultaneous comparison of 3 or more groups. A real example is given to illustrate the proposed method using EQS, a popular SEM software program.     

一类Fuzzy非线性规划的极大熵方法

利用近几年发展起来的求解不可微规划的极大熵方法 ,将一类Fuzzy非线性规划问题转化为可微的无约束规划问题 ,给出一求解Fuzzy非线性规划精确最优解的算法 .     

An Alternative Approach for Nonlinear Latent Variable Models

In the last decades there has been an increasing interest in nonlinear latent variable models. Since the seminal paper of Kenny and Judd, several methods have been proposed for dealing with these kinds of models. This article introduces an alternative approach. The methodology involves fitting some third-order moments in addition to the means and covariances. This article discusses how the model equations can be formulated and how several standard tests, like the model fit and Lagrange multiplier tests, can be performed. The new method compares favorably with the maximum likelihood method in several studies and can provide evidence of interaction that earlier approaches might ignore.     

Modeling of Nonrecursive Structural Equation Models With Categorical Indicators

Nonrecursive structural equation models generally take the form of feedback loops, involving 2 latent variables that are connected by 2 unidirectional paths, 1 starting with each variable and terminating in the other variable. Nonrecursive models belong to a larger class of path models that require the use of instrumental variables (IVs) to achieve model identification. Prior research has focused on SEM parameter estimation with IVs when indicators were continuous and normally distributed. Much less is known about how estimators function in the presence of categorical indicators, which are commonly used in the social sciences, such as with cognitive and affective instruments. In this study, there was specific interest in comparing the 2-stage least squares (2SLS) estimator and its categorical variant to other recommended estimators. This study compares the performance of several estimation approaches for fitting structural equation models with categorical indicator variables when IVs are necessary to obtain proper model estimates. Across conditions, 1 extension of the nonlinear 2SLS (N2SLS) approach, the nonlinear 3-stage least squares (N3SLS), which accounts for correlated errors among regressors within each model (as does the N2SLS), as well as correlations of errors across models, which N2SLS does not, appears to work the best among methods compared.     

BP神经网络在多维非线性函数拟合中的应用

为了解决常规方法拟合多维非线性函数的预测结果精度较低问题,采用非线性映射能力较强的BP神经网络进行多维非线性函数拟合。通过对BP神经网络结构及训练过程的分析,依据给定的两维非线性函数构建BP神经网络模型,并利用MATLAB软件对BP神经网络进行预测。仿真结果表明,BP神经网络对于两维非线性函数有良好的拟合能力,拟合误差小、收敛速度快,也使该方法具有较好的推广性。     

Constrained Maximum Likelihood Estimation for Two-level Mean and Covariance Structure Models

Maximum likelihood is commonly used for estimation of model parameters in analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in maximum likelihood analysis. Nonlinear constraints could be encountered in complicated applications. In this paper we develop an EM-type algorithm for estimating model parameters with both linear and nonlinear constraints. The empirical performance of the algorithm is demonstrated by a Monte Carlo study. Application of the algorithm for linear constraints is illustrated by setting up a two-level mean and covariance structure model for a real two-level data set and running an EQS program.     

基于MATLAB GUI的概率分布程序设计

为了直观认识随机变量概率分布的特性及其经验分布拟合的动态变化,提出了基于MATLAB GUI的可视化思想,通过算例说明了该程序的编制与实现方法.     

一种构造Prolog程序子句本体的方法

为了使Prolog推理引擎可以从一定程度的语义上来区分子句,通过概念图的形式对Prolog程序的子句建立本体.子句本体的概念模型通过其谓词参数和头子目标来建立,而头子目标是指出现在规则体首部并且位于任何谓词调用之前的子目标集合.所提出的方法把一个Prolog程序转换成包含其子句本体的Prolog CG程序.实验表明,通过对Prolog子句建立本体,可以尽早地去除与当前目标明显不匹配的子句,从而使得目标求解树的规模减小.在概念图的描述形式下,子句本体使得Prolog程序的语义更清晰可读,在某些情况下能明显加快程序的求解过程.     

Deformation-based freeform feature reconstruction in reverse engineering

For reconstructing a freeform feature from point cloud, a deformation-based method is proposed in this paper. The freeform feature consists of a secondary surface and a blending surface. The secondary surface plays a role in substituting a local region of a given primary surface. The blending surface acts as a bridge to smoothly connect the unchanged region of the primary surface with the secondary surface. The secondary surface is generated by surface deformation subjected to line constraints, i.e., character lines and limiting lines, not designed by conventional methods. The lines are used to represent the underlying information of the freeform feature in point cloud, where the character lines depict the feature＇s shape, and the limiting lines determine its location and orientation. The configuration of the character lines and the extraction of the limiting lines are discussed in detail. The blending surface is designed by the traditional modeling method, whose intrinsic parameters are recovered from point cloud through a series of steps, namely, point cloud slicing, circle fitting and regression analysis. The proposed method is used not only to effectively and efficiently reconstruct the freeform feature, but also to modify it by manipulating the line constraints. Typical examples are given to verify our method.     

Inference on Two-Part Latent Variable Analysis Model With Multivariate Longitudinal Data

Semicontinuous variable analysis is a widely appreciated statistical method in such disciplines as social science, medicines, and economics. In detecting underlying structure and representing possible interrelationships, statistical analysis using a two-part model is appropriated. In this paper, we present a general extension of two-part model to the situation where the unobserved factors are included in the two parts to interpret external variability in semicontinuous variable. Auxiliary information on these factors is manifested by continuous responses via measurement model, while the interrelationships among factors are exploited through structural equation model. Moreover, under longitudinal setting, dynamic characteristics of responses between any two occasions are represented by transition model. Procedures for model fitting, parameter estimation, model selection and prediction are developed within the Bayesian paradigm. Markov Chains Monte Carlo method is used to implement posterior analysis. Empirical results including a simulation and a real example are used to illustrate the proposed methodology.     

多项式约束优化问题的一种新方法

针对带多项式不等式约束和多项式等式约束优化问题,提出了一个新的求全局最优解的方法：首先将其不等式约束转化为等式约束,然后按K-T条件将其化为解方程组问题,再利用软件包Wsolve求出方程组的解,从而获得原问题的全局最优解.实例计算表明,该方法在解这类优化问题时,是简明和行之有效的.     

基于高阶模糊推理系统的曲线拟合研究

利用高阶模糊推理系统进行曲线拟合．首先构建高阶模糊推理系统模型,然后基于梯度下降的学习算法对高阶模糊推理系统模型的权值进行改进,最后进行曲线拟合,并与基于最小二乘的曲线拟合结果进行比较．结果表明,高阶模糊推理系统比基于最小二乘的曲线拟合具有更强的非线性处理能力,更适用于曲线拟合．     

用程序解加权最小二乘拟合多项式的系数

本文首先简单介绍了最小二乘法原理和利用加权最小二乘法求拟合多项式的系数问题,然后利用C语言设计求解加权最小二乘拟合多项式系数的程序．只要输入给定的数据点、相应的权重和所求拟合多项式的次数,运行该程序后就能准确地、高精度地得到拟合多项式的系数．     

一种求解非线性方程组的混沌优化算法

本文针对非线性方程组的求解问题提出一种混合算法，将方程组转换成一个优化问题。将优化问题的非线性共轭梯度法与混沌优化方法相结合，提出了一种新的混合优化算法。该算法能使非线性共轭梯度法跳出局部最优，最终获得全局最优。算法的收敛性也进行了证明，数值结果表明该算法是有效的。     

一种最优自适应增益非线性观测器的锂电池SOC估算方法

如今电动汽车的发展十分迅速,其动力电池荷电状态SOC关系到锂电池及整车系统的安全、可靠运行,因为SOC表明了电池剩余电量。由于SOC是一个不可直接测量的非线性变量,因此设计一种精度高、可行性强的算法具有十分重要的意义。提出一种最优自适应增益非线性观测器（OAGNO）,用差分进化算法（DE）对观测器参数进行寻优。为了验证该方法的先进性,对型号为NCR18650GA的三元锂电池进行工况实验,结果表明,相比无迹卡尔曼滤波（UKF）,最优自适应非线性状态观测器具有更高的精度,误差在3%左右。     

Predictive control of a class of bilinear systems based on global off-line models

INTRODUCTION Bilinear systems are a kind of important nonlinear systems with relatively simple structure, and many industrial processes can be described as a bilinear system. Thus research on the control of this kind of systems is very important. On the other hand, model predictive control (MPC) (Clarke et al., 1987) has been widely used in industrial applications and many predictive control methods focusing on bilinear systems are emerging (Bloemen et al., 2001; Fontes et al., 2004; He…     

一种求解非线性方程组的混沌优化算法

本文针对非线性方程组的求解问题提出一种混合算法,将方程组转换成一个优化问题.利用优化问题的非线性共轭梯度法与混沌优化方法相结合,提出了一种新的混合优化算法.该算法能使非线性共轭梯度法跳出局部最优,最终获得全局最优.通过对算法的收敛性的证明及数值分析,结果表明该算法是有效的.     

一种求解非线性方程组的混沌优化算法

本文针对非线性方程组的求解问题提出一种混合算法,将方程组转换成一个优化问题。利用优化问题的非线性共轭梯度法与混沌优化方法相结合,提出了一种新的混合优化算法。该算法能使非线性共轭梯度法跳出局部最优,最终获得全局最优。算法的收敛性也进行了证明,数值结果表明该算法是有效的。     

线性l1问题的一个算法

本文首先将线性l_1问题转化为一个与之等价的约束线性l_1问题,然后研究它的算法,最后给出一个算例。     

Penalized interior point approach for constrained nonlinear programming

A penalized interior point approach for constrained nonlinear programming is examined in this work. To overcome the difficulty of initialization for the interior point method, a problem equivalent to the primal problem via incorporating an auxiliary variable is constructed. A combined approach of logarithm barrier and quadratic penalty function is proposed to solve the problem. Based on Newton's method, the global convergence of interior point and line search algorithm is proven.Only a finite number of iterations is required to reach an approximate optimal solution. Numerical tests are given to show the effectiveness of the method.