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101.
E. M. Abbasov O. A. Dyshin B. A. Suleimanov 《Computational Mathematics and Mathematical Physics》2008,48(12):2194-2210
A method is proposed for constructing stable approximate wavelet decompositions of weak solutions to boundary value problems for the unsteady porous-medium flow equation with discontinuous coefficients and inexact data. The method is based on the general scheme for finite-dimensional approximation in Tikhonov regularization and on multiresolution analysis with basis functions defined as the product of one-dimensional Daubechies wavelets. 相似文献
102.
We establish the asymptotic normality of the squared L
2-norm of the approximation error of a linear wavelet estimator of the density of a distribution. The calculations are based on the smallness of correlations between the coefficients of the high-frequency part of the multiresolution expansion of the estimator.Supported by the FCT Foundation (Portugal) in the framework of the project Probability and Statistics (2000–2002), Centro de Matematica, Universidade da Beira Interior.__________Translated from Lietuvos Matematikos Rinkinys, Vol. 45, No. 2, pp. 184–207, April–June, 2005. 相似文献
103.
紧支撑三元正交小波滤波器的参数化 总被引:1,自引:0,他引:1
高维小波分析是分析和处理多维数字信号的有力工具.非张量积多元小波被广泛地应用在模式识别、纹理分析和边缘检测等领域.本文给出方体域上三元正交滤波器的一种参数化构造算法,三元小波滤波器的这种构造方法使我们能更方便地研究非张量积的三元正交小波.最后给出数值算例. 相似文献
104.
变压器中的一些放电和机械故障会产生异常声音,可用于故障检测。据此,本文提出基于可听声的变压器放电和机械故障诊断方法。针对机械故障声音与变压器本体噪声特征相似易混淆的问题提出改进小波包-BP神经网络算法,与传统小波包-BP神经网络算法相比声音的识别率提高了5.7%。为提高声音识别系统的泛化性,提出基于梅尔对数频谱和卷积神经网络的声音识别算法。两种算法相互验证,提高了系统的可靠性。在真实变压器油箱中模拟了不同类型放电和机械故障。试验结果表明,本文提出的两种方法能成功识别放电和机械故障的声音,声音识别率分别为99.6%和97.57%。 相似文献
105.
The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)). 相似文献
106.
Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain 下载免费PDF全文
To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation. 相似文献
107.
We study the approximation of the inverse wavelet transform using Riemannian sums.We show that when the Fourier transforms of wavelet functions satisfy some moderate decay condition,the Riemannian sums converge to the function to be reconstructed as the sampling density tends to infinity.We also study the convergence of the operators introduced by the Riemannian sums.Our result improves some known ones. 相似文献
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