基于IGA和Hammerstein模型的非线性系统广义预测控制

针对非线性程度较高的系统,设计了一种广义预测控制器。该设计基于Hammerstein模型的动静态可分离特性,首先利用具有全局搜索能力的免疫遗传算法（IGA,Immune Genetic Algorithm）在线辨识模型的一些关键参数,然后运用广义预测控制策略实现对该系统的预测控制。仿真试验结果表明,该设计能够准确预测,而且稳定性好、稳态误差小。     

半参数广义线性模型惩罚似然估计的几个性质

本文在给出了半参数广义线性模型惩罚似然估计及惩罚准似然估计的基础上,得到了下面几个性质:(Ⅰ)极小惩罚似然估计及惩罚准似然估计等价于惩罚加权最小二乘估计;(Ⅱ)估计的收敛性及加速收敛法;(Ⅲ)估计的一种 Bayes 解释。     

基于变分贝叶斯算法的线性变参数系统辨识

线性变参数系统（LPV）将多阶段、非线性的过程建模转化为线性多模型的辨识问题,是解决非线性过程建模的一个有效手段。由于实际工业过程存在各种干扰因素,导致被建模系统呈现随机性及模型参数的不确定性。针对这一问题,考虑采用变分贝叶斯（VB）算法对LPV模型进行辨识。该算法首先给定参数相应的先验分布,通过最大化目标函数的下界,从而估计得到参数的后验分布。不仅可实现对参数的点估计,同时量化了估计值的不确定性。针对典型二阶过程和连续搅拌反应釜（CSTR）,运用提出的算法进行仿真实验,表明了该贝叶斯估计方法的优越性。     

融合线性和非线性残差的一次性垃圾分类模型

为应对一次性塑料垃圾难检测的问题,应用卷积神经网络提出一次性塑料垃圾分类模型。该模型在预处理阶段模拟手工提取方式捕获线性和非线性残差信息,在残差特征学习阶段通过卷积神经网络融合线性和非线性残差特征。仿真结果表明:线性和非线性残差特征具备较优的分类能力,深层次网络有利于融合各类残差且学习捕获高级语义特征信息,本模型的检测分类准确率为75.84%,优于传统HOG模型约8%。     

自适应广义一般模型控制

针对一般模型控制的缺点,提出了两种改进的自适应广义一般模型控制方法.它们把传统的一般模型控制推广到相对阶大于1 同时又具有时变参数的复杂非线性过程.第1种控制策略主要利用强跟踪滤波器直接在线估计时变参数,来修正过程模型;另一种方法是将所有干扰因素归结为输入等价干扰,通过估计它来实现对过程的前馈控制.仿真实验结果验证了所提出方法的有效性.     

广义线性子波反演影响参数分析

本文从褶积模型出发,用理论子波合成最小相位性质的地震道记录,以广义线性反演方法为基础,通过在反演的过程中改变不同的参数,得到不同条件下的反演结果。并将反演结果和合成地震记录的理论子波比较,得出该方法的一般结论。     

线性与非线性黏滞阻尼支撑减震效果的比较

黏滞阻尼器的速度指数取值制约着阻尼支撑减震效果,需确定合理的速度指数取值范围．从工程设计应用出发,按2种思路比较房屋在分别设置线性与非线性黏滞阻尼支撑后产生的结构响应、减震效果、柱轴力、经济性和安全性．研究结果表明：非线性黏滞阻尼支撑在小震下较为经济有效,在大震下抗震安全度较高且方便相关支撑与连接设计的结论．     

基于PFC3D的砂土线性模型细观参数敏感性研究

准确地标定参数是使用商用离散元软件PFC合理建模的前提和关键步骤。采用PFC3D对砂土线性模型试样共进行了80次三轴压缩试验模拟,分别改变颗粒间摩擦系数、颗粒有效模量、颗粒刚度比和试样初始孔隙率等细观参数,探究不同围压下细观参数的变化对试验结果（最大偏应力）的敏感性。结果表明,颗粒间摩擦系数对试验结果有明显影响,初始孔隙率在>0.32%时对试验结果有明显影响。     

不同化学组成的熟料混合粉磨增强效应的非线性统计模型

应用活性矿物掺料增强效应模型,建立了定量分析不同化学组成熟料混合磨 水泥时质量较差的熟料在混磨水泥中的增强效应的非线性统计模型,并将该模型方法推广到不同窑型熟料混合粉磨制备水泥的情况,验证了该模型的合理性和有效性,在给定两种熟料的情况下,依据此模型可以快捷的判断这两种熟料的混合粉磨的可行性,并确定最佳增强效应时的混合比例,从而可以指导生产实践。     

SEMI‐PARAMETRIC ESTIMATION OF LINEAR COINTEGRATING MODELS WITH NONLINEAR CONTEMPORANEOUS ENDOGENEITY

This article considers linear cointegrating models with unknown nonlinear short‐run contemporaneous endogeneity. Two estimators are proposed to estimate the linear cointegrating parameter after the nonlinear endogenous component is estimated by local linear regression approach. Both the proposed estimators are shown to have the same mixed normal limiting distribution with zero mean and smaller asymptotic variance than the fully modified ordinary least squares and instrumental variables estimators. Monte Carlo simulations are used to evaluate the finite sample performance of our proposed estimators, and an empirical application is also included.     

TRANSFORMED POLYNOMIALS FOR NONLINEAR AUTOREGRESSIVE MODELS OF THE CONDITIONAL MEAN

This article proposes a flexible set of transformed polynomial functions for modelling the conditional mean of autoregressive processes. These functions enjoy the same approximation theoretic properties of polynomials and, at the same time, ensure that the process is strictly stationary, is ergodic, has fading memory and has bounded unconditional moments. The consistency and asymptotic normality of the least‐squares estimator is easily obtained as a result. A Monte Carlo study provides evidence of good finite sample properties. Applications in empirical time‐series modelling, structural economics and structural engineering problems show the usefulness of transformed polynomials in a wide range of settings.     

DATA AUGMENTATION AND DYNAMIC LINEAR MODELS

Abstract. We define a subclass of dynamic linear models with unknown hyperpara-meter called d -inverse-gamma models. We then approximate the marginal probability density functions of the hyperparameter and the state vector by the data augmentation algorithm of Tanner and Wong. We prove that the regularity conditions for convergence hold. For practical implementation a forward-filtering-backward-sampling algorithm is suggested, and the relation to Gibbs sampling is discussed in detail.     

(LG)型参数估计的若干讨论

在已知(LG)型参数的最小二乘估计与GM估计的基础上,本文提出了改进最小二乘估计的两大估计——Stein型压缩估计与主成分估计。这是线性模型参数估计的重要问题,而改进(LG)型参数的最小二乘估计的专题研究,至今还是一个有待进一步深入的新论题,本文只是初步结果。     

ON THE UNBIASEDNESS PROPERTY OF AIC FOR EXACT OR APPROXIMATING LINEAR STOCHASTIC TIME SERIES MODELS

Abstract. A rigorous analysis is given of the asymptotic bias of the log maximum likelihood as an estimate of the expected log likelihood of the maximum likelihood model, when a linear model, such as an invertible, gaussian ARMA ( p, q ) model, with or without parameter constraints, is fit to stationary, possibly non-gaussian observations. It is assumed that these data arise from a model whose spectral density function either (i) coincides with that of a member of the class of models being fit, or, that failing, (ii) can be well-approximated by invertible ARMA ( p, q ) model spectral density functions in the class, whose ARMA coefficients are parameterized separately from the innovations variance. Our analysis shows that, for the purpose of comparing maximum likelihood models from different model classes, Akaike's AIC is asymptotically unbiased, in case (i), under gaussian or separate parametrization assumptions, but is not necessarily unbiased otherwise. In case (ii), its asymptotic bias is shown to be of the order of a number less than unity raised to the power max { p, q } and so is negligible if max { p, q } is not too small. These results extend and complete the somewhat heuristic analysis given by Ogata (1980) for exact or approximating autoregressive models.     

材料科学研究中常用的热力学模型评述

热力学模型在材料科学的理论研究中具有重要的作用,在实践中具有指导和预测作用,大大避免了实践中的盲目性。文章介绍了材料科学研究中常用的热力学模型:理想溶液近似、正规溶液近似、双亚点阵模型、团簇变分法等,分析了各种热力学模型的特点及应用,强调了相图计算在材料科学研究中的重要性。     

IDENTIFIABILITY AND DISTINGUISHABILITY TESTING FOR LINEAR MODELS IN HETEROGENEOUS CATALYSIS

If several values of the parameters of a model are associated with the same behavior, then the model is not identifiable and there is no hope of estimating a unique best value for the parameter vector from experimental data. Similarly, if several models with different structures correspond to the same behavior, then these models are not distinguishable and there is no hope of selecting a structure that best corresponds to the experimental data. Two methods for testing linear models for identifiability and distinguish ability are recalled and applied to types of catenary compartmental models encountered for instance when studying the isobutane-isobutene-hydrogen system by transient isotopic tracing.     

QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS

We study inference and diagnostics for count time series regression models that include a feedback mechanism. In particular, we are interested in negative binomial processes for count time series. We study probabilistic properties and quasi‐likelihood estimation for this class of processes. We show that the resulting estimators are consistent and asymptotically normally distributed. These facts enable us to construct probability integral transformation plots for assessing any assumed distributional assumptions. The key observation in developing the theory is a mean parameterized form of the negative binomial distribution. For transactions data, it is seen that the negative binomial distribution offers a better fit than the Poisson distribution. This is an immediate consequence of the fact that transactions can be represented as a collection of individual activities that correspond to different trading strategies.     

A NOTE ON ASYMPTOTIC INFERENCE IN AUTOREGRESSIVE MODELS WITH ROOTS ON THE UNIT CIRCLE

Abstract. An estimation and inference procedure is proposed for parameters of the p th order autoregressive model with roots both on the unit circle and outside the unit circle. The procedure is motivated by the fact that the parameter estimates of the nonstationary part of the model have higher order consistency properties than the parameter estimates of the stationary part. The procedure allows the use of the known asymptotic distributional results of purely nonstationary models and purely stationary models. Only ordinary least squares routines are needed.