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灰色模型在城市环境噪声预测中的应用 总被引:3,自引:1,他引:2
根据灰色系统理论,对太原环境声序列分析表明,该序列是一非单调摆动序列或有饱和的S形序列,建立了相应的Verbulst灰色预测模型,结果表明该模型预测精度较高,最大残差小于0.54%。 相似文献
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《振动与冲击》2019,(20)
针对高速自动机运动形态的多行程特点,提出一种分时段规范变量残差分析(Phase-partitioned Canonical Variate Dissimilarity Analysis, PCVDA)方法用于高速自动机的动态特性监测。通过建立整个行程与短时瞬态冲击信号的对应关系,将冲击信号划分为多个时段;采用正弦波辅助经验模态分解(Sinusoid-assisted Empirical Mode Decomposition, SEMD)将每个时段的冲击信号分解为高频和低频成分,分别计算两种成分的过去和未来数据的规范变量的残差,建立基于高低频成分的PCVDA模型监测高速自动机在不同时段的动态特性。对某12.7 mm高速自动机的监测结果验证了PCVDA模型的有效性。 相似文献
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为准确预测我国生产安全事故发展趋势,本文在传统GM(1,1)模型和马尔科夫模型的基础上,结合二者优点提出改进灰色马尔科夫预测模型,并以2005—2018年全国生产安全事故起数为原始序列探讨了改进模型的实际应用。区别于传统灰色残差修正理论,选取灰色模型预测结果的相对误差作为修正指标,2次应用马尔科夫模型对相对误差状态和误差符号状态进行优化预测,并使用平均相对误差和小概率误差对模型进行精度检验。结果表明,改进GM(1,1)-Markov模型预测结果的相对误差为3.0%,较单一灰色预测模型预测误差减少19.5%,预测精度显著提高,同时预测得到2019年我国生产安全事故起数为479。 相似文献
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针对多尺度模糊熵(multi-scale fuzzy entropy,MFE)分析时间序列复杂性时捕捉不到高频组分信息的局限,提出一种新的基于小波包模糊熵(wavelet packet fuzzy entropy,WPFE)的故障特征提取方法。该方法利用小波包对信号的低频和高频成分进行分解,应用模糊熵对各频带分量进行量化得到特征向量,因而能提取更全面、准确的故障信息。以往复压缩机传动机构为研究对象,将小波包模糊熵作为特征提取工具,通过振动信号提取不同位置轴承间隙大故障的特征向量,利用支持向量机作为分类器,与多尺度模糊熵进行对比分析,验证了该方法的有效性。 相似文献
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当前 ,对混凝土质量判断主要依赖于对接收信号的处理。和小波分析相比 ,小波包分析的优势在于其不仅对低频部分进行分解 ,对高频部分也进行分解 ,并能根据被分析信号的特征 ,自适应地选择相应频带 ,使之与信号频谱相匹配 ,从而提高时频分辨率。本文利用小波分析方法 ,对超声接收信号进行小波包分解 ,分别提取各个频率成分的信号特征 ,并对小波包分解系数重构 ,求出各频带信号的总能量。通过构造特征向量 ,进行归一化处理 ,用实验统计方法确定特征值和容差范围 ,通过对不同混凝土试块中传播的超声波进行分析 ,判断出混凝土的内部质量 相似文献
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Maximum likelihood estimation of change point from stationary to nonstationary in autoregressive models using dynamic linear model 下载免费PDF全文
Reza Sheikhrabori Majid Aminnayeri Mona Ayoubi 《Quality and Reliability Engineering International》2018,34(1):27-36
Change point estimation is a useful concept in time series models that could be applied in several fields such as financing, quality control. It helps to decrease costs of decision making and production by monitoring stock market and production lines, respectively. In this paper, the maximum likelihood technique is developed to estimate change point at which the stationary AR(1) model changes to a nonstationary process. Filtering and smoothing of dynamic linear model are used to estimate unknown parameters after change point. We also assume that correlation exists between samples' statistics. Simulation results show the effectiveness of the proposed estimators to estimate the change point of stationary. In addition based on Shewhart control chart, filtering has a better accuracy in comparison to smoothing. A real example is provided to illustrate the application. 相似文献
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Weiguo Gong Ke Wen Lifang He Lihua Cheng Yong Li 《International Journal of Thermophysics》2012,33(10-11):2237-2241
The auto regressive (AR) model of time series is utilized in this paper to recognize a human and nonhuman from pyroelectric infrared (PIR) signals. Through the wavelet transform, the signals are reconstructed by removing the noise from the original signals. The coefficients of the AR model are selected as the features for human and nonhuman recognition and calculated by the Burg algorithm. The classification experiments of a human and nonhuman are performed with a support vector machine. The recognition results for different PIR signals using the proposed AR-based features show high performance with an optimal recognition rate, which is up to 94.6 % and higher than that of the traditional time domain feature and transform domain method, such as the wavelet entropy and wavelet entropy of the double-density dual-tree complex wavelet transform. 相似文献
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基于小波分析的惯性传感器信号Kalman滤波 总被引:1,自引:0,他引:1
针对光电跟踪系统惯性传感器信号特点,本文提出通过小波分析的方式确定相关Kalman滤波的模型及参数.该方法利用小波分析的优良特性,采用先将信号进行去噪处理,然后对去噪后的信号进行AR建模.根据小波去噪后的信号比较接近真实信号,将得到的观测噪声方差乘以一个小于1的系数后作为系统的过程噪声方差,从而确定模型的噪声参数.仿真实验结果表明,该方法不仅对惯性传感器的静态数据有很好的效果,而且对其动态观测数据也有良好的效果.同时,该方法不仅对光电跟踪系统有效,而且还具有一定的通用性. 相似文献
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A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra 总被引:3,自引:0,他引:3
Wensheng Cai Yankun Li Xueguang Shao 《Chemometrics and Intelligent Laboratory Systems》2008,90(2):188-194
Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A modified method of uninformative variable elimination (UVE) was proposed for variable selection in NIR spectral modeling based on the principle of Monte Carlo (MC) and UVE. The method builds a large number of models with randomly selected calibration samples at first, and then each variable is evaluated with a stability of the corresponding coefficients in these models. Variables with poor stability are known as uninformative variable and eliminated. The performance of the proposed method is compared with UVE-PLS and conventional PLS for modeling the NIR data sets of tobacco samples. Results show that the proposed method is able to select important wavelengths from the NIR spectra, and makes the prediction more robust and accurate in quantitative analysis. Furthermore, if wavelet compression is combined with the method, more parsimonious and efficient model can be obtained. 相似文献
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肌电信号是与神经肌肉活动有关的生物电的体现,肌电信号的模式识别是肌电应用的基础。利用现代功率谱估计中的参数模型法,对从掌长肌、肱桡肌、尺侧腕屈肌和肱二头肌采集的4路表面肌电信号建立AR参数模型,并提取其AR模型参数作为信号的特征,构造特征矢量,提供给基于距离测度的Mahalanobis距离分类器进行模式分类,能够成功地识别出握拳、展拳、腕内旋、腕外旋、屈腕、伸腕、前臂内旋、前臂外旋8种动作模式。实验表明,该方法识别率高、鲁棒性好,为肌电等非平稳生物电信号的模式识别提供了一种新方法。 相似文献
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针对故障齿轮振动信号的非平稳特征和包含强烈噪声,很难提取故障特征频率的情况,提出了基于双树复小波和奇异差分谱的故障诊断方法。首先将非平稳的故障振动信号通过双树复小波分解为几个不同频段的分量;由于噪声的影响,从各个分量的频谱中难以准确地得到故障频率。然后对包含故障特征的分量构建Hankel矩阵并进行奇异值分解,求奇异值差分谱曲线,确定奇异值个数进行SVD重构降噪,由此实现对故障特征信息的提取。最后再求希尔伯特包络谱,便能准确地得到故障频率。实验结果和工程应用表明,该方法可以有效地提取齿轮的故障特征信息,验证了方法的可行性和有效性。 相似文献
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ZHAORong-zhen ZHANGYou-yun 《国际设备工程与管理》2004,9(3):131-138
The method to enhance the precision of a grey model GM (1,1) for predicting the development of vibration severity of a pump is inwestigated. The rectifying procedures involve the structure and the parameters regarding GM(1,1).A new model based on GM(1,1),which is GM(E,1,1), is proposed. In GM(E,1,1),the distribution of relative errors ratios between the original series and predicting series obtained by the mean of GM(1,1)are considered in special points to set up the threshold and adjusting coefficients to control the modified action and the rectified amount based on distribution of the original series. The case shows that GM(E,1,1) is good at predicting the vibration severity development of the pump. 相似文献