共查询到20条相似文献，搜索用时 140 毫秒
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针对遗忘因子递推最小二乘法的遗忘因子为固定值而造成锂电池参数辨识结果稳定性和锂电池等效电路模型精度无法同时兼顾的问题,提出一种将模糊算法与遗忘因子递推最小二乘法相结合的融合算法——模糊遗忘因子递推最小二乘法,使得遗忘因子在模糊控制器的作用下实现动态可变.通过选取一阶RC模型作为锂电池的等效电路模型,并基于固定遗忘因子递推最小二乘法和模糊遗忘因子递推最小二乘法,分别对一阶RC模型进行参数辨识,然后分别将参数辨识结果带入模型中进行模型端电压误差计算.仿真结果表明,相较于遗忘因子取值为1的固定遗忘因子递推最小二乘算法,基于模糊遗忘因子递推最小二乘法的锂电池模型端电压平均绝对误差值下降了0.000 85 V,最大误差绝对值下降了0.074 2V.相较于遗忘因子取值为0.9的固定遗忘因子递推最小二乘法,模糊遗忘因子递推最小二乘法参数辨识结果的稳定性有非常显著地提升. 相似文献
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最小二乘参数估计的递推算法是系统参数辨识中最基本、最成熟的方法。文章首先介绍了最小二乘法的递推算法原理和本识别系统的框架流程图,然后针对文章的算法分别阐述了服从N（0,1）正态分布自相关随机噪声v（k）的产生方法。文章着重介绍了利用C语言编程对一个简单系统的参数辨识实现最小二乘参数估计的递推算法,详细说明了本系统各个环节的C语言实现,并通过matlab仿真对数据进行了详细的分析。从仿真实验结果可以看出,试验数据符合试验要求,系统的参数辨识实现最小二乘参数估计的递推算法的效果令人满意。 相似文献
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《浙江大学学报(A卷英文版)》2017,(4)
目的:开路电压是基于模型的电池荷电状态估计的必要参数,其测试耗时大、效率低。本文旨在测试各种电压松弛时间的荷电状态-开路电压关系,研究其对开路电压法和等效电路模型的荷电状态估计准确度的影响,提高开路电压测试效率。创新点:1.通过电路解构方法,将二阶阻容电路分解为简单路,运用二阶段递推最小二乘法辨识电路模型的参数;2.基于递推最小二乘法和卡尔曼滤波算法,建立电路参数辨识和荷电状态估计的的联合自适应算法,研究电池电压松弛时间对基于等效电路模型的荷电状态估计的影响。方法:1.通过电路解构技术和理论推导,构建辨识二阶阻容等效电路参数的二阶段递推最小二乘法辨识方法(图2和公式(4)~(9));2.将二阶段递推最小二乘法和扩展卡尔曼滤波器集成,建立适应工况变化的电池模型参数辨识和状态估计的联合算法(图3);3.通过电池测试,建立多温度和多电压松弛时间的荷电状态与开路电压的关系,驱动自适应联合算法,获得既保证荷电状态估计准确度,又缩短开路电压测试时间的电压松弛时间。结论:1.二阶段递推最小二乘法既能简化矩阵计算,又能够保证电路参数的辨识非负性;2.联合自适应算法能够适应工况变化辨识模型参数和估计荷电状态;3.联合自适应算法的结果表明,5 min的电压松弛时间既能保证荷电状态估计性能,又能极大地提高开路电压测试效率。 相似文献
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利用输出比输入快速采样方法研究ARMA模型的盲辨识问题,提出了最小二乘盲辨识方法。通过选择适当的快采样率及归一化系统模型参数之后,仅利用快采样得到的输出信号实现了系统模型参数的估计。仿真例子表明所提盲辨识方法的有效性。 相似文献
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以比例阀的输出为系统输入,液位值为系统输出,对液位控制系统进行ARX建模研究。选用AIC准则作为系统模型阶次的选择原则,以最小二乘法来辨识模型参数,辨识了系统的ARX模型。模型的预测输出和实际输出的比较结果证实了ARX建模在液位控制系统中的有效性。 相似文献
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《武汉工程职业技术学院学报》2017,(1)
通过建立先导式电液比例减压阀的数学模型和Simulink模型,通过对Simulink模型施加正弦扫频信号,获得系统的频率特性,并采用加权最小二乘曲线拟合法对系统进行辨识,给出了出系统数学模型的辨识参数,将系统的辨识模型与Simulink模型进行了对比仿真,验证了辨识模型的准确性,为以后比例减压阀的控制提供合适的系统描述。 相似文献
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林琦 《宁德师专学报(自然科学版)》2007,19(3):235-238
电机理论、电机模型以及电机参数辨识的方法在共同进步．随着电机辨识理论的发展，辨识方法的逐步完善，电机参数在现实中的应用十分广泛．同时电机参数辨识在电机控制中具有极其重要的意义．简要阐述几种当前典型的电机参数中的频响法、最小二乘法和卡尔曼滤波法等一些辨识方法的原理和框图． 相似文献
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《实验室研究与探索》2018,(12)
针对永磁同步电动机运行时参数变化的问题,提出了一种基于FFRLS的变遗忘因子最小二乘法的电动机参数辨识方法以及实验设计方案。该方法引入误差概念,使遗忘因子与引入的误差成反比关系,根据实时数据进行动态调整,进而解决了FFRLS单一遗忘因子切换问题,改善辨识系统的收敛速度和稳定性能。利用MATLAB/Simulink对变遗忘因子最小二乘法进行了仿真验证;搭建了以FPGA为核心的永磁同步电动机在线参数辨识平台。实验结果证明,所提方法可以有效地实现电动机参数辨识,可以应用在永磁同步电动机的高性能控制中。 相似文献
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为了减小数字滤波器设计工作量,利用MATLAB软件,采用窗函数法、频率采样法及最优等波纹法设计FIR数字滤波器.通过比较不同设计方法得到的滤波器阶数以及幅频特性曲线,结果表明,最优等波纹法可大大减少了计算的复杂程度,所设计的滤波器简单,是FIR滤波器设计中的最优方法. 相似文献
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Carlos G. Forero Alberto Maydeu-Olivares David Gallardo-Pujol 《Structural equation modeling》2013,20(4):625-641
Factor analysis models with ordinal indicators are often estimated using a 3-stage procedure where the last stage involves obtaining parameter estimates by least squares from the sample polychoric correlations. A simulation study involving 324 conditions (1,000 replications per condition) was performed to compare the performance of diagonally weighted least squares (DWLS) and unweighted least squares (ULS) in the procedure's third stage. Overall, both methods provided accurate and similar results. However, ULS was found to provide more accurate and less variable parameter estimates, as well as more precise standard errors and better coverage rates. Nevertheless, convergence rates for DWLS are higher. Our recommendation is therefore to use ULS, and, in the case of nonconvergence, to use DWLS, as this method might converge when ULS does not. 相似文献
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Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is to use polychoric correlations and fit the models using methods such as unweighted least squares (ULS), maximum likelihood (ML), weighted least squares (WLS), or diagonally weighted least squares (DWLS). In this simulation evaluation we study the behavior of these methods in combination with polychoric correlations when the models are misspecified. We also study the effect of model size and number of categories on the parameter estimates, their standard errors, and the common chi-square measures of fit when the models are both correct and misspecified. When used routinely, these methods give consistent parameter estimates but ULS, ML, and DWLS give incorrect standard errors. Correct standard errors can be obtained for these methods by robustification using an estimate of the asymptotic covariance matrix W of the polychoric correlations. When used in this way the methods are here called RULS, RML, and RDWLS. 相似文献
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This study describes three least squares models to control for rater effects in performance evaluation: ordinary least squares (OLS); weighted least squares (WLS); and ordinary least squares, subsequent to applying a logistic transformation to observed ratings (LOG-OLS). The models were applied to ratings obtained from four administrations of an oral examination required for certification in a medical specialty. For any single administration, there were 40 raters and approximately 115 candidates, and each candidate was rated by four raters. The results indicated that raters exhibited significant amounts of leniency error and that application of the least squares models would change the pass-fail status of approximately 7% to 9% of the candidates. Ratings adjusted by the models demonstrated higher reliability and correlated slightly higher than observed ratings with the scores on a written examination. 相似文献
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In structural equation models, outliers could result in inaccurate parameter estimates and misleading fit statistics when using traditional methods. To robustly estimate structural equation models, iteratively reweighted least squares (IRLS; Yuan & Bentler, 2000) has been proposed, but not thoroughly examined. We explore the large-sample properties of IRLS and its effect on parameter recovery, model fit, and aberrant data identification. A parametric bootstrap technique is proposed to determine the tuning parameters of IRLS, which results in improved Type I error rates in aberrant data identification, for data sets generated from homogenous populations. Scenarios concerning (a) simulated data, (b) contaminated data, and (c) a real data set are studied. Results indicate good parameter recovery, model fit, and aberrant data identification when noisy observations are drawn from a real data set, but lackluster parameter recovery and identification of aberrant data when the noise is parametrically structured. Practical implications and further research are discussed. 相似文献
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郭志军 《邢台职业技术学院学报》2010,27(5):79-82
文章在提出线性回归问题的基础上,用最小二乘法求回归方程。通过实例应用软件Mathematica进行分析求解,两种方法的比较可以看出,Mathematica在求解线性回归问题中可大大地提高办公效率。 相似文献
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相关性分析技术在软件度量中的应用 总被引：2，自引：0，他引：2
丁剑洁 《陕西教育学院学报》2008,24(1):100-103
数据分析是软件度量活动中的关键环节,不合理的数据分析会造成资源的浪费,更严重的是根据度量结果得到的决策信息会给软件组织带来误导,从而失去了软件度量的意义。相关性分析是数据分析中的一种重要方法,它能帮助确定软件不同属性之间是否存在关系。本文探讨了三种相关性分析技术在软件度量中的应用,归纳了相关性分析技术的使用步骤,并给出应用实例。 相似文献
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给出了求解非线性最小二乘的修正拟牛顿方法。该方法结合了非单调搜索技术和结构化拟牛顿法的思想,提出了一种新的求解非线性最小二乘的修正拟牛顿法,并证明了该方法的全局收敛性。 相似文献
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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. 相似文献