Tag：Residual

Time：2021428
1. Basic theory Deep residual shrinkage networkIt is based on three parts, including residual network, attention mechanism and soft thresholding. Its functional features include: 1) As soft thresholding is a common step in signal denoising algorithm, deep residual shrinkage network is more suitable for strong noise and high redundancy data. At the same time, the […]

Time：2021323
Link to the original text:http://tecdat.cn/?p=20666 Stock price prediction has been widely concerned by investors, government, enterprises and scholars. However, the nonlinearity and non stationarity of data make the development of prediction model a complex and challenging task. In this article, I’ll explain how toGARCH，EGARCHAndGJRGARCHModel andMonteCarloSimulation is used in combination to establish an effective prediction model. […]

Time：202131
01. Preface We talked about multiple linear regression. In this article, let’s talk about gradual regression. What is stepwise regression? It’s literally a stepbystep return. We know that the element in multiple regression refers to the independent variable, and multiple variables are multiple independent variables, namely multiple X. One of the questions we need to […]

Time：2021218
In this paper, a new deep learning algorithm deep residual shrinkage network is discussed. 1. The basis of deep residual shrinkage network It can be seen from the name that the deep residual shrinkage network is an improved method of the deep residual network. Its feature is “shrinkage”, which refers to soft thresholding, which is […]

Time：2021218
We talked about heteroscedasticity and how to use graphical method to judge whether there is heteroscedasticity. This article talks about how to use statistical method to judge whether there is heteroscedasticity. There are many statistical methods to test heteroscedasticity. In this section, we only talk about the white test which is more common and commonly […]

Time：2021217
RESNET, a residual network, has won the best paper award of IEEE Conference on computer vision and pattern recognition in 2016, with 38295 academic citations in Google. Deep residual shrinkage network is an improved version of deep residual network, which is actually the integration of deep residual network, attention mechanism and soft threshold function. To […]

Time：2021213
Are there any new ideas of convolution neural network applied to fault diagnosis? Amaze2’s answer – Zhihuhttps://www.zhihu.com/question/265223166/answer/1329919383 The vibration monitoring signal of mechanical equipment often contains a lot of noise, which affects the effect of fault diagnosis and needs to be overcome. In order to solve the above problems, a soft threshold function is added […]

Time：2021212
How to combine convolution neural network with mechanical vibration signal? Do fault pattern recognition.? Amaze2’s answer – Zhihuhttps://www.zhihu.com/question/279888242/answer/1329908863 Now there are a lot of literatures in this field. Amway one, the deep residual shrinkage network is a convolution neural network, adding a soft threshold function, suitable for noisy mechanical vibration signal feature extraction, in order […]

Time：2021211
Is there a new variant of CNN that can improve the accuracy of the model? Amaze2’s answer – Zhihu https://www.zhihu.com/question/397944899/answer/1314983195 We can just introduce a onedimensional CNN model called deep residual shrinkage networks. The deep residual shrinkage network is originally used in fault diagnosis based on onedimensional vibration signal, which is an improvement of the […]

Time：2021210
Can deep learning be used for pattern recognition of time series? For example, mechanical fault pattern recognition based on noise signal, have you done this research? The answer of the great distance – Zhihu https://www.zhihu.com/question/40992219/answer/1325531901 Deep residual shrinkage network is a kind of mechanical fault pattern recognition method for noise and vibration signals. As shown […]

Time：2021115
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for SkeletonBased Action Recognition source Author unit meeting Paper address code Jiangnan University ICCV 2019 Paper address Not yet Innovation A spatial residual layer is introduced to capture and fuse spatiotemporal featuresIn previous work, spatiotemporal layer includes spatial graph convolution and temporal […]

Time：202116
Excuse me, the deep residual network has been so powerful, let’s improve the image classification of graduate students from where to innovate? Amaze2’s answer – Zhihu https://www.zhihu.com/questio… Maybe we can get some inspiration from the deep learning methods in other fields. For example,Residual shrinkage networkIt is an improvement of residual network RESNET. It was originally […]