思维进化算法在估计瞬时单位线参数中的应用

文章结合思维进化算法(Mind Evolutionary Computation,MEC),采用加权残差绝时值和最小准则对瞬时单拉线模型参数进行估计,实例表明使用此方法率定的参数还原的径流过程与实测径流过程拟合优于其他方法.     

基于和声搜索算法的瞬时单位线参数优化估计

和声搜索(Harmony Search,HS)算法是一种启发式优化算法,通过反复调整和声记忆库中解的变量,使待优化函数目标值随着迭代次数的增加不断收敛,从而来完成优化。此算法概念简单、可调参数少、容易实现。文章将基于和声搜索,并采用加权残差绝对值和最小准则对瞬时单位线模型参数进行估计,实例表明使用此方法率定的参数还原的径流过程与实测径流过程拟合相较其它方法更佳。     

径流长期预报的支持向量机方法

支持向量机是近年来提出的一种新的机器学习算法,在解决小样本、非线性及高维模式识别问题中具有独特的优势.针对径流形成过程的不确定性和复杂性,提出了基于支持向量机分类模型的径流长期预报方法.相关研究表明,该方法是可行的,具有预测精度高和良好的可靠性等优点.     

径流长期预报的支持向量机方法

支持向量机是近年来提出的一种新的机器学习算法,在解决小样本、非线性及高维模式识别问题中具有独特的优势。针对径流形成过程的不确定性和复杂性,提出了基于支持向量机分类模型的径流长期预报方法。相关研究表明,该方法是可行的,具有预测精度高和良好的可靠性等优点。     

降雨径流与水土流失的关系分析

水土流失属于环境问题,多年以来,人们一直在研究水土流失问题,分析水土流失发生的各种原因。造成水土流失的因素有很多,例如:土壤类型、降雨径流、地质地形条件、植被覆盖率、降雨量、土地整体利用等,其中降雨径流是造成水土流失的重要原因,因此,有必要研究水土流失和降雨径流的内在规律和关系,帮助我们分析降水径流和水土流失实际状况,进而制定科学的方法来解决水土流失问题,保护赖以生存的环境。     

降雨径流与水土流失的关系分析

水土流失属于环境问题,多年以来,人们一直在研究水土流失问题,分析水土流失发生的各种原因。造成水土流失的因素有很多,例如：土壤类型、降雨径流、地质地形条件、植被覆盖率、降雨量、土地整体利用等,其中降雨径流是造成水土流失的重要原因,因此,有必要研究水土流失和降雨径流的内在规律和关系,帮助我们分析降水径流和水土流失实际状况,进而制定科学的方法来解决水土流失问题,保护赖以生存的环境。     

岩溶管道径流公式研究

如何准确计算地下水在岩溶-裂隙管道中的径流成为了岩溶含水层地下水计算和模拟的关键,虽然目前国际上使用较为广泛的岩溶管道模拟方法 Conduit Flow process(CFP)相比传统的等效多孔介质渗流计算方法在计算精度上有了很大的提高,但该方法仍然忽略了岩溶管道发育形态特征,仅将管道截面概化为圆形,且计算时未分流态。针对CFP法存在的上述问题,本研究从管道形成及形态特征入手,对岩溶管道径流公式进行研究,该方法在岩溶径流计算及岩溶裂隙含水层地下水渗流计算具有广泛应用前景,为相应数值模拟软件开发提供了理论基础。     

岩溶地区的工程地质勘察方法探讨

根据多年的工程实践与学习研究,对目前我国岩溶地区主要的岩土工程勘察分析方法及探测技术进行了探讨,并对各种相应的勘察技术方法的适用范围和特点作了分析。文章结合岩溶补给区、径流区、排泄区的水文地质条件和岩溶发育特点的不同,并结合工程实例介绍岩溶补给区、径流区和排泄区的普遍工程地质勘察方法。     

基于UG的汽车门盖件参数化建模方法研究

文章简要分析了参数化建模方法必要性及其基本原则,介绍了CATIA参数化建模的思路,重点提出了UG中汽车门盖件的参数化建模方法,并以某发罩内板为例,详细阐述了参数化建模过程.     

气流干燥对烟丝中糖苷类致香成分的影响

本文对气流干燥过程中的参数的变化对烟丝糖苷类致香成分的影响进行了研究。研究发现,在实际的生产过程中,可以采用一些有针对性的方法来调节气流干燥过程中的参数,尽可能的保留烟丝中的糖苷类致香成分,采用这些方法进一步的优化我国卷烟香气生产过程中对质量的要求。     

基于模型分析的液压伺服系统性能参数预测

为了提高液压伺服系统的控制精度,文章对液压伺服系统中的一些非线性参数进行了预测与估计。参数估计之前,首先对目标液压工作系统进行了建模与参数化描述,抽取了其控制和工作模型,计算并推导了模型中主要的液压参数直接的数学关系,如液压受力分析、受力传递函数、噪声信号过滤函数等。在此基础上,采用最小二乘估计算法对液压伺服系统中的非线性参数进行预测,给出了详细的参数分析过程和预测参数推导过程,建立了主要参数的估计计算公式。最后,对所估计的非线性参数进行了仿真和测试。结果表明,文章所选择的参数预测结果与实际的运行结果基本吻合,预测算法参数估计误差小。     

超超临界参数机组的发展和热力分析

文章介绍了超超临界参数技术的发展历史和现状,对超超临界参数机组的热力参数和效率进行了分析。     

基桩声波透射法检测各判据精度探讨

声速、声时、声幅、主频这四个声测参数是判断桩基完整性的主要依据。而在实际桩检中,各参数都不能达到足够的精度评判出桩身质量的好坏,必须经过综合比较加以确定,仅评某一参数的异常来作出判定容易得出相左的结论。并且PSD、声速参数可以归为同一参数。     

The effect of parameter uncertainty on forecast variances and confidence intervals for unit root and trend stationary time-series models

In this paper I describe the effect of parameter uncertainty on the way conditional forecast variances grow as the forecast horizon increases. Without parameter uncertainty, forecast variances for the unit root model grow linearly with the forecast horizon while with the trend stationary model they are bounded. With parameter uncertainty, however, I find that for both the unit root and the trend stationary models, forecast variances grow with the square of the forecast horizon so that uncertainty grows at a much faster rate than without parameter uncertainty.     

Experimental evidence of bank runs as pure coordination failures

We investigate how coordination requirement, measured by the coordination parameter, affects the occurrence of miscoordination-based bank runs in controlled laboratory environments. We identify an indeterminacy region of the coordination parameter such that games with the parameter within the region have varying coordination outcomes and exhibit persistent path dependence. Experimental economies with the parameter above (below) the region stay close or converge to the run (non-run) equilibrium. Switches between the two equilibria occur even with fixed economic fundamentals. The experimental results are well accounted for by a version of the evolutionary algorithm that uses experimentation rates estimated from the experimental data.     

Parameter estimation and bias correction for diffusion processes

This paper considers parameter estimation for continuous-time diffusion processes which are commonly used to model dynamics of financial securities including interest rates. To understand why the drift parameters are more difficult to estimate than the diffusion parameter, as observed in previous studies, we first develop expansions for the bias and variance of parameter estimators for two of the most employed interest rate processes, Vasicek and CIR processes. Then, we study the first order approximate maximum likelihood estimator for linear drift processes. A parametric bootstrap procedure is proposed to correct bias for general diffusion processes with a theoretical justification. Simulation studies confirm the theoretical findings and show that the bootstrap proposal can effectively reduce both the bias and the mean square error of parameter estimates, for both univariate and multivariate processes. The advantages of using more accurate parameter estimators when calculating various option prices in finance are demonstrated by an empirical study.     

Nonlinear minimization estimators in the presence of cointegrating relations

In this paper, we consider estimation of a long-run and a short-run parameter jointly in the presence of nonlinearities. The theory developed establishes limit behavior of minimization estimators of the long- and short-run parameters jointly. Typically, if the long-run parameter that is present in a cointegrating relationship is estimated, its estimator will be superconsistent. Therefore, we may conjecture that the joint minimization estimation of both parameters jointly will result in the same limit distribution for the short-run parameter as if the long-run parameter was known. However, we show that unless a regularity condition holds, this intuition is false in general. This regularity condition, that clearly holds in the standard linear case, is identical to the condition for validity of a two-step Granger–Engle type procedure. Also, it is shown that if the cointegrated variables are measured in deviation from their averages, the standard asymptotic normality result (that one would obtain if the long-run parameter was known) holds.     

On least favourable two point priors and minimax estimators under absolute error loss

Summary In case of absolute error loss we investigate for an arbitrary class of probability distributions, if or if not a two point prior can be least favourable and a corresponding Bayes estimator can be minimax when the parameter is restricted to a closed and bounded interval of ℝ. The general results are applied to several examples, for instance location and scale parameter families are considered. We give examples for which, independent of the length of the parameter interval, no two point priors exist. On the other hand examples are given having a least favourable two point prior when the parameter interval is sufficiently small.     

Asymptotically optimal tests for some growth curve models under non-normal error structure

This paper considers growth curve models consisting of two stages. In the first stage we have a regression model with random parameter. The second stage is given by a linear model for the mean of the random parameter of the first stage. In the present paper we prove asymptotic optimality of tests for linear functions of the mean of the random parameter under non-normal error. Received: September 1999     

Tikhonov regularization for nonparametric instrumental variable estimators

We study a Tikhonov Regularized (TiR) estimator of a functional parameter identified by conditional moment restrictions in a linear model with both exogenous and endogenous regressors. The nonparametric instrumental variable estimator is based on a minimum distance principle with penalization by the norms of the parameter and its derivatives. After showing its consistency in the Sobolev norm and uniform consistency under an embedding condition, we derive the expression of the asymptotic Mean Integrated Square Error and the rate of convergence. The optimal value of the regularization parameter is characterized in two examples. We illustrate our theoretical findings and the small sample properties with simulation results. Finally, we provide an empirical application to estimation of an Engel curve, and discuss a data driven selection procedure for the regularization parameter.