A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

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)).     

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

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.     

Convergence of Riemannian sums of inverse wavelet transforms

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.     

基于小波域马尔可夫随机场模型的压缩传感图像重构

目前在压缩传感重构算法中利用图像的可稀疏性表示先验知识,从比奈奎斯特采样少得多的观测值中恢复原始图像。除了稀疏性之外,邻域系数的相关性也可以作为先验知识加速重构算法收敛。为了克服目前算法中没有利用邻域系数相关性的缺点,提出了基于小波域马尔可夫随机场模型的压缩传感图像重构算法,根据显著性度量对变换系数进行分类得到具有马尔可夫性的初始掩模,利用ICM算法完成掩模优化,实现系数更新,并将算法与未考虑邻域相关性的算法进行了比较。实验结果证明了算法的有效性。     

基于小波域扩散滤波的弱小目标检测

分析了基于小波变换进行弱小目标检测的基本思想,利用小波变换的多尺度多分辨率特性,结合小波变换系数的方向特性和扩散滤波扩散方向的可选择性,提出了基于小波域扩散滤波的弱小目标检测算法。采用该算法对不同尺度、不同方向的小波系数分别进行扩散滤波,取得了较好的效果。仿真试验结果表明：该算法能在Gaussian噪声背景和不均匀背景下实现对对比度为2%的微弱目标的检测。     

湍流信号分析中小波基的对比和选取

采用小波分析处理湍流脉动信号时,不同的小波基往往会带来不同的分析结果.为了尽可能准确地提取湍流流动的多尺度特征,研究并建立湍流信号处理中适宜小波基选取的合理方法是十分必要的.基于湍流时频特性,结合信息熵理论,建立了以品质因数和特征谱熵值为评价指标的小波基选取方法.以计算分析三组不同湍流信号的小波基为例,结果进一步验证了...     

Wavelet operational matrix method for solving the Riccati differential equation

A Haar wavelet operational matrix method (HWOMM) was derived to solve the Riccati differential equations. As a result, the computation of the nonlinear term was simplified by using the Block pulse function to expand the Haar wavelet one. The proposed method can be used to solve not only the classical Riccati differential equations but also the fractional ones. The capability and the simplicity of the proposed method was demonstrated by some examples and comparison with other methods.     

L~2(R~n)上的半正交多小波框架

本文研究L2(Rn)上伸缩矩阵A满足|detA|1的半正交多小波框架.本文得到半正交和严格半正交框架的一系列性质及刻画.本文证明半正交Parseval多小波框架与广义多分辨分析(GMRA)Parseval多小波框架是等价的.特别地,本文利用最小频率支撑(MSF)多小波框架和小波集,构造若干半正交多小波框架的例子.     

Numerical inversion of laplace transform via wavelet in partial differential equations

This article presents a rational Haar wavelet operational method for solving the inverse Laplace transform problem and improves inherent errors from irrational Haar wavelet. The approach is thus straightforward, rather simple and suitable for computer programming. We define that P is the operational matrix for integration of the orthogonal Haar wavelet. Simultaneously, simplify the formulae of listing table (Chen et al., Journal of The Franklin Institute 303 (1977), 267–284) to a minimum expression and obtain the optimal operation speed. The local property of Haar wavelet is fully applied to shorten the calculation process in the task. The operational method presented in this article owns the advantages of simpler computation as well as broad application. We still can obtain satisfying solution even under large matrix. Moreover, we do not have numerically unstable problems. © 2013 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 30: 536–549, 2014