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THE NUMERICAL METHODS FOR SOLVING EULER SYSTEM OF EQUATIONS IN REPRODUCING KERNEL SPACE H^2(R)

Journal of Computational Mathematics 2001年第3期19卷 327-336页

作者： Wu, BY Zhang, QL Harbin Inst Technol Dept Math Harbin 150001 Peoples R China

Provides information on a study which presented a numerical method for solving Euler system of equations in reproducing kernel space. Definition and properties of reproducing kernel space; Construction of reproducing ... 详细信息

关键词： Euler system of equations reproducing kernel method finite difference method wavelet function

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维普期刊数据库

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Parameterization of 3-channel non-separable 2-D wavelets and filters

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Science in China(Series F) 2004年第3期47卷 362-371页

作者： GAOXieping ZHONGHua InformationEngineeringCollege XiangtanUniversityXiangtan411105China HunanBranch IndustrialandCommercialBankofChinaChangsha410001China

We propose a complete parameterization presentation of the 3-channelbivariate non-separable orthogonal FIR filter, and describe the sufficient condition ofgenerating continuous wavelet bases. Given the results above, ... 详细信息

关键词： wavelet function FIR filter non-separable

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维普期刊数据库

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Simulation and prediction of monthly accumulated runoff,based on several neural network models under poor data availability

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Research in Cold and Arid Regions 2018年第6期10卷 468-481页

作者： JianPing Qian JianPing Zhao Yi Liu XinLong Feng DongWei Gui College of Mathematics and System Sciences Xinjiang University State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Cele National Station of Observation and Research for Desert–Grassland Ecosystem Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences

Most previous research on areas with abundant rainfall shows that simulations using rainfall-runoff modes have a very high prediction accuracy and applicability when using a back-propagation(BP), feed-forward, multilayer perceptron artificial neural network(ANN). However, in runoff areas with relatively low rainfall or a dry climate, more studies are needed. In these areas—of which oasis-plain areas are a particularly good example—the existence and development of runoff depends largely on that which is generated from alpine regions. Quantitative analysis of the uncertainty of runoff simulation under climate change is the key to improving the utilization and management of water resources in arid areas. Therefore, in this context, three kinds of BP feed-forward, three-layer ANNs with similar structure were chosen as models in this *** the oasis–plain region traverse by the Qira River Basin in Xinjiang, China, as the research area, the monthly accumulated runoff of the Qira River in the next month was simulated and predicted. The results showed that the training precision of a compact wavelet neural network is low; but from the forecasting results, it could be concluded that the training algorithm can better reflect the whole law of samples. The traditional artificial neural network(TANN) model and radial basis-function neural network(RBFNN) model showed higher accuracy in the training and prediction stage. However, the TANN model, more sensitive to the selection of input variables, requires a large number of numerical simulations to determine the appropriate input variables and the number of hidden-layer neurons. Hence, The RBFNN model is more suitable for the study of such problems. And it can be extended to other similar research arid-oasis areas on the southern edge of the Kunlun Mountains and provides a reference for sustainable water-resource management of arid-oasis areas.

关键词： oasis artificial neural network radial basis function wavelet function runoff simulation

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Feature Extract Based on Improved Fuzzy Neural Network

Feature Extract Based on Improved Fuzzy Neural Network

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2010 Chinese Control and Decision Conference

作者： Che Chang,Hu Dan School of Mechanical Engineering and Automation,Xihua University,ChengDu,610039

＜正＞By changing the construction of the fuzzy neural network based on wavelet basis function,an improved fuzzy neural network is introduced for feature extraction of fault *** the selection of wavelet base and the orthogonal least-square（OLS） algorithm,an improved fuzzy neural network is described for feature extraction so as to improve the system ***,according to the cutting force sample data,it is proved that this method has fast convergence and excellent optimization feature subset property,reduces the significant over-fitting problem.

关键词： Neural Network Feature Extract Cutting Force wavelet function

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wavelet neural network predication for epidemic outbreak

Wavelet neural network predication for epidemic outbreak

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2009中国控制与决策会议

作者： Li Xiao-yi XU Zhao-di Li Zhen-peng School of Mathematics and Systems Science,Shenyang Normal University,Shenyang 110034,China

＜正＞In order to predict the epidemic outbreak,this paper presented a wavelet neural network *** wavelet neural network designing theory framework was also provided:wavelet function is used as activation function in recessive layer,the connection weights are decided by wavelet function ***,the presented method efficiency and feasibility were discussed by a simulation example,some key problems and research directions also summarized..

关键词： wavelet neural networks wavelet function Fitting test Classification and prediction

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An Improved Fault Diagnosis Method Based on Deep wavelet Neural Network

An Improved Fault Diagnosis Method Based on Deep Wavelet Neu...

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第30届中国控制与决策会议

作者： Yibo Liu Qingyu Yang Dou An Yongqiang Nai Zhiqiang Zhang School of Electronic and Information Engineering Xi'an Jiaotong University SKLMSE lab School of Electronic and Information EngineeringXi'an Jiaotong University MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University

Deep learning has been successfully applied to the field of fault diagnosis in recent *** to the advantages of deep belief network（DBN） in fitting nonlinear complex systems and the ability of wavelet analysis in time-frequency analysis,in this paper,an improved fault diagnosis method based on a deep wavelet neural network（DWNN）,which combines the DBN with morlet activation functions,is proposed for fault diagnosis of reciprocating compressor.A five-layer DBN using sigmoid,tanh,rectified linear unit（ReLU） and morlet wavelet functions as the activation functions of hidden layers separately is proposed for fault diagnosis of reciprocating *** the contrast,a three-layer back propagation neural network（BPNN） using the same four activation functions separately is proposed for fault diagnosis of reciprocating *** experimental results show that,the fault diagnosis rate of five-layer DBN is higher than the three-layer *** method based on DWNN can make the fault diagnosis rate reach 100% within short *** with using other activation functions,the DWNN architecture requires less epochs to train the model.

关键词： Reciprocating compressor fault diagnosis deep belief network wavelet function

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