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将计算机发生的“扰动-响应”曲线的基值与正态分布伪随机数迭加,可以获得人工模拟的“扰动-响应”实验曲线。本文分别运用矩量法、加权矩量法、传递函数法、傅立叶分析法及时间域最小二乘拟合法对模拟实验曲线加以处理与参数估值,并采用 Monte-Carlo 法对这五种数据处理方法作出比较与评价。模拟实验计算结果表明,傅立叶分析法和时间域最小二乘拟合法精度最高,是值得推荐的数据处理方法。 相似文献
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离散数据拟合模型的研究与实现 总被引:1,自引:0,他引:1
最小二乘支持向量机引入到离散数据拟合中,代替传统的最小二乘法解决离散数据拟合问题。推导了用于函数估计的最小二乘支持向量机算法,构建了基于最小二乘支持向量机的离散数据拟合模型,并对电机数据拟合进行了研究。结果表明,最小二乘支持向量机拟合离散数据比最小二乘法精度更高、拟合效果更好。 相似文献
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基于LS-SVM的小样本费用智能预测 总被引:5,自引:3,他引:5
最小二乘支持向量机引入最小二乘线性系统到支持向量机中,代替传统的支持向量机采用二次规划方法解决函数估计问题。该文推导了用于函数估计的最小二乘支持向量机算法,构建了基于最小二乘支持向量机的智能预测模型,并对机载电子设备费用预测进行了研究。结果表明最小二乘支持向量机具有比多元对数回归更高的小样本费用预测精度。 相似文献
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基于接收信号强度指示(RSSI)定位模型,提出了一种目标节点位置的精确计算方法。将RSSI定位问题所描述的非线性优化函数转化为线性最小二乘法估计问题,将定位结果直接用代数解表示。分别提出了目标节点信号发射强度已知和未知下的非约束线性最小二乘(ULLS)定位方法。同时对非约束线性最小二乘法下的参数进一步优化,提出了约束线性最小二乘法以提高定位精度。仿真验证了该定位计算方法的有效性,测试了不同信号强度噪声对定位误差的影响。结果同时表明,约束线性最小二乘法比非约束线性最小二乘法的定位误差更小,非常接近于定位结果的克拉美罗下界值(CRLB)。 相似文献
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针对参数辨识中最小二乘法(LS)存在的缺点,讨论了一种用迭代的松弛算法对最小二乘辨识的改进方法-广义最小二乘(GLS)辨识,并介绍了其基于Matlab的仿真和分析方法。首先简述参数辨识的概念、最小二乘法辨识存在的主要缺点和广义最小二乘法的基本原理,之后简要介绍了Matlab系统辨识工具箱及其中参数辨识的实现方法,最后结合实例给出相应的仿真程序及其结果分析,仿真结果表明:该方法辨识精度高,明显优于最小二乘辨识。 相似文献
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应用最小二乘法辨识闭环系统 总被引:4,自引:0,他引:4
研究了有色噪声扰动下闭环系统参数的无偏估计问题.基于偏差补偿最小二乘辨识方法,
提出了一种用于闭环辨识的偏差补偿最小二乘法.这种方法不需要对噪声建模,即可获得闭
环系统前向通道和反馈通道传递函数中参数的渐近无偏估计. 相似文献
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基于分段直线拟合的伪随机码相位测量法 总被引:1,自引:0,他引:1
针对目前PN码相位测量抗干扰能力有限的问题,提出了一种新的PN码相位测量方法;该方法以PN码的相关函数为基础,以峰值点为分界点对相关峰曲线两侧分别做最小二乘直线拟合;然后求出两条直线的交点坐标,其中横坐标与零相偏参考值的差值就是PN码相位的估值;该方法与目前常用的最小二乘同步法和三点二次插值PN码相位测量法相比较,算法复杂度增加有限;通过仿真数据可以得出,新的PN码测量方法在信噪比较低的情况下能得到比最小二乘同步法和三点二次插值法更加稳定和精确的估值结果。 相似文献
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《Computational statistics & data analysis》2007,51(12):3644-3662
A useful class of partially nonstationary vector autoregressive moving average (VARMA) models is considered with regard to parameter estimation. An exact maximum likelihood (EML) approach is developed on the basis of a simple transformation applied to the error-correction representation of the models considered. The employed transformation is shown to provide a standard VARMA model with the important property that it is stationary. Parameter estimation can thus be carried out by applying standard EML methods to the stationary VARMA model obtained from the error-correction representation. This approach resolves at least two problems related to the current limited availability of EML estimation methods for partially nonstationary VARMA models. Firstly, it resolves the apparent impossibility of computing the exact log-likelihood for such models using currently available methods. And secondly, it resolves the inadequacy of considering lagged endogenous variables as exogenous variables in the error-correction representation. Theoretical discussion is followed by an example using a popular data set. The example illustrates the feasibility of the EML estimation approach as well as some of its potential benefits in cases of practical interest which are easy to come across. As in the case of stationary models, the proposed EML method provides estimated model structures that are more reliable and accurate than results produced by conditional methods. 相似文献
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Jos Alberto Mauricio 《Computational statistics & data analysis》2006,50(12):3644-3662
A useful class of partially nonstationary vector autoregressive moving average (VARMA) models is considered with regard to parameter estimation. An exact maximum likelihood (EML) approach is developed on the basis of a simple transformation applied to the error-correction representation of the models considered. The employed transformation is shown to provide a standard VARMA model with the important property that it is stationary. Parameter estimation can thus be carried out by applying standard EML methods to the stationary VARMA model obtained from the error-correction representation. This approach resolves at least two problems related to the current limited availability of EML estimation methods for partially nonstationary VARMA models. Firstly, it resolves the apparent impossibility of computing the exact log-likelihood for such models using currently available methods. And secondly, it resolves the inadequacy of considering lagged endogenous variables as exogenous variables in the error-correction representation. Theoretical discussion is followed by an example using a popular data set. The example illustrates the feasibility of the EML estimation approach as well as some of its potential benefits in cases of practical interest which are easy to come across. As in the case of stationary models, the proposed EML method provides estimated model structures that are more reliable and accurate than results produced by conditional methods. 相似文献
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On the application of cross correlation function to subsample discrete time delay estimation 总被引:2,自引:0,他引:2
Cross correlation function (CCF) of signals is an important tool of multi-sensors signal processing. Parabola functions are commonly used as parametric models of the CCF in time delay estimation. The parameters are determined by fitting samples near the maximum of the CCF to a parabola function. In this paper we analyze the CCF for the stationary processes of exponential auto-correlation function, with respect to two important types of sensor sampling kernels. Our analysis explains why the parabola is an acceptable model of CCF in estimating the time delay. More importantly, we demonstrate that the Gaussian function is a better and more robust approximation of CCF than the parabola. This new approximation approach leads to higher precision in time delay estimation using the CCF peak locating strategy. Simulations are also carried out to evaluate the performance of the proposed estimation method for different sample window sizes and signal to noise ratios. The new method offers significant improvement over the current parabola based method. 相似文献
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针对GM(1,1)幂模型从离散的参数估计到连续的预测函数所产生的固有误差, 提出一种新的分数阶离散GM(1,1)幂模型, 并针对可能存在的病态性, 利用正则化算法替代最小二乘法估计部分参数以提高参数估计的精度; 为了提高模型的预测精度, 提出新的累加阶数及幂参数的确定方法. 对工业废水排放率及城市用水量两个实例的预测结果表明, 所提出的模型及确定参数的方法对于振荡时间序列有着很好的预测精度. 相似文献
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针对时变自回归滑动平均(TVARMA)模型参数谱估计容易出现谱峰漂移的问题,提出一种基于组合目标函数和遗传算法的TVARMA模型参数估计方法,并将之应用于飞行器结构响应序列的谱估计。首先,通过长自回归方法和增广最小二乘方法获得TVARMA模型参数初始估计值;其次,依据连续函数极值条件推导模型参数的频域约束条件并结合罚函数方法构造组合目标函数;最后,采用遗传算法对模型参数进行优化获得使组合目标函数最小的参数值作为TVARMA模型参数的最优估计。应用结果表明:该方法可以克服谱峰漂移现象,提高模型在时域和时频域的建模精度。 相似文献
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针对小数据集条件下的贝叶斯网络(Bayesian network,BN)参数估计困难问题,提出了一种基于变权重迁移学习(DWTL)的BN参数学习算法。首先,利用MAP和MLE方法学习得到目标域初始参数和各源域参数;然后根据不同源域数据样本贡献的不同计算源权重因子;接着基于目标域样本统计量与小数据集样本阈值的关系设计了目标域初始参数和源域参数的平衡系数;最后,基于上述参数、源权重因子和平衡系数计算得到新的目标参数。在实验研究中,通过对经典BN模型的参数学习问题验证了DWTL算法的有效性;针对小数据集下的轴承故障诊断问题,相较于传统迁移学习(LP)算法,DWTL算法学习精度提高了10%。实验结果表明:所提出的算法能够较好地解决样本数据集在相对稀缺条件下的目标参数建模问题。 相似文献