scholarly journals Two-Dimensional Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bo Jia ◽  
Dong Li ◽  
Zhisong Pan ◽  
Guyu Hu
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Studies ◽  
Back Propagation ◽  
Image Data ◽  
Network Models ◽  
Support Vector ◽  
Two Dimensional ◽  
Neural Network Models ◽  
Efficiency And Effectiveness ◽  
Learning Machine

Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

A Set of New Hermite Kernel Functions in Kernel Extreme Learning Machine and Application in Human Action Recognition

2019 ◽  
Vol 33 (12) ◽  
pp. 1955014 ◽  
Author(s):  
Xueping Liu ◽  
Xingzuo Yue
Keyword(s):  
Extreme Learning Machine ◽  
Action Recognition ◽  
Structural Information ◽  
Image Data ◽  
Human Action Recognition ◽  
Human Action ◽  
Kernel Functions ◽  
Support Vector ◽  
Learning Speed ◽  
Learning Machine

The kernel function has been successfully utilized in the extreme learning machine (ELM) that provides a stabilized and generalized performance and greatly reduces the computational complexity. However, the selection and optimization of the parameters constituting the most common kernel functions are tedious and time-consuming. In this study, a set of new Hermit kernel functions derived from the generalized Hermit polynomials has been proposed. The significant contributions of the proposed kernel include only one parameter selected from a small set of natural numbers; thus, the parameter optimization is greatly facilitated and excessive structural information of the sample data is retained. Consequently, the new kernel functions can be used as optimal alternatives to other common kernel functions for ELM at a rapid learning speed. The experimental results showed that the proposed kernel ELM method tends to have similar or better robustness and generalized performance at a faster learning speed than the other common kernel ELM and support vector machine methods. Consequently, when applied to human action recognition by depth video sequence, the method also achieves excellent performance, demonstrating its time-based advantage on the video image data.

scholarly journals An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4024 ◽  
Author(s):  
Jae-Neung Lee ◽  
Yeong-Hyeon Byeon ◽  
Sung-Bum Pan ◽  
Keun-Chang Kwak
Keyword(s):  
Extreme Learning Machine ◽  
Back Propagation ◽  
Principal Component ◽  
Personal Identification ◽  
Two Dimensional ◽  
Ecg Signals ◽  
Peak Points ◽  
Learning Machine ◽  
Ecg Database ◽  
Two Dimensional Images

We herein propose an EigenECG Network (EECGNet) based on the principal component analysis network (PCANet) for the personal identification of electrocardiogram (ECG) from human biosignal data. The EECGNet consists of three stages. In the first stage, ECG signals are preprocessed by normalization and spike removal. The R peak points in the preprocessed ECG signals are detected. Subsequently, ECG signals are transformed into two-dimensional images to use as the input to the EECGNet. Further, we perform patch-mean removal and PCA algorithm similar to the PCANet from the transformed two-dimensional images. The second stage is almost the same as the first stage, where the mean removal and PCA process are repeatedly performed in the cascaded network. In the final stage, the binary quantization, block sliding, and histogram computation are performed. Thus, this EECGNet performs well without the use of back-propagation to obtain features from the visual content. We constructed a Chosun University (CU)-ECG database from an ECG sensor implemented by ourselves. Further, we used the well-known MIT Beth Israel Hospital (BIH) ECG database. The experimental results clearly reveal the good performance and effectiveness of the proposed method compared with conventional algorithms such as PCA, auto-encoder (AE), extreme learning machine (ELM), and ensemble extreme learning machine (EELM).

scholarly journals MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

2020 ◽  
Vol 12 (6) ◽  
pp. 962 ◽  
Author(s):  
Changyu Liu ◽  
Xiaodong Huang ◽  
Xubing Li ◽  
Tiangang Liang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Snow Cover ◽  
Back Propagation ◽  
Network Models ◽  
Support Vector ◽  
Learning Models ◽  
Neural Network Models ◽  
Fractional Snow Cover ◽  
Machine Learning Models

To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.

scholarly journals Prediction of Residual Gas Content during Coal Roadway Tunneling Based on Drilling Cuttings Indices and BA-ELM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zhenhua Yang ◽  
Hongwei Zhang ◽  
Sheng Li ◽  
Chaojun Fan
Keyword(s):  
Coal Seam ◽  
Extreme Learning Machine ◽  
Back Propagation ◽  
Prediction Method ◽  
Gas Content ◽  
Percentage Error ◽  
Support Vector ◽  
Prevention Measures ◽  
Residual Gas ◽  
Learning Machine

In order to predict the residual gas content in coal seam in front of roadway advancing face accurately and rapidly, an improved prediction method based on both drilling cuttings indices and bat algorithm optimizing extreme learning machine (BA-ELM) was proposed. The test indices of outburst prevention measures (drilling cuttings indices, residual gas content in coal seam) during roadway advancing in Yuecheng coal mine were first analyzed. Then, the correlation between drilling cuttings indices and residual gas content was established, as well as the neural network prediction model based on BA-ELM. Finally, the prediction result of the proposed method was compared with that of back-propagation (BP), support vector machine (SVM), and extreme learning machine (ELM) to verify the accuracy. The results show that the average absolute error, the average absolute percentage error, and the determination coefficient of the proposed prediction method of residual gas content in coal seam are 0.069, 0.012, and 0.981, respectively. This method has higher accuracy than other methods and can effectively reveal the nonlinear relationship between drilling cuttings indices and residual gas content. It has prospective application in the prediction of residual gas content in coal seam.

scholarly journals Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1850
Author(s):  
Long Chen ◽  
Xiaojie Zhi ◽  
Hai Wang ◽  
Guanjin Wang ◽  
Zhenghua Zhou ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Traffic Accidents ◽  
Back Propagation ◽  
Learning Technologies ◽  
Support Vector ◽  
Driver Fatigue ◽  
Learning Machine ◽  
Fatigue Driving

Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM.

scholarly journals Extreme Learning Machine for Reservoir Parameter Estimation in Heterogeneous Sandstone Reservoir

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Cao ◽  
Jucheng Yang ◽  
Yan Wang ◽  
Dan Wang ◽  
Yancui Shi
Keyword(s):  
Parameter Estimation ◽  
Extreme Learning Machine ◽  
Back Propagation ◽  
Support Vector ◽  
Core Data ◽  
Porosity And Permeability ◽  
Sandstone Reservoir ◽  
Learning Machine ◽  
Reservoir Parameter ◽  
Propagation Network

This study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by conventional petrophysical methods in wells without core data. 4950 samples from 8 wells with core data have been used to train and validate the neural network, and robust ELM algorithm provides fast and accurate prediction results, which is also testified by comparison with BP (back propagation) network and SVM (support vector machine) approaches. The network model is then applied to estimate porosity and permeability for the remaining wells. The predicted attributes match well with the oil test conclusions. Based on the estimations, reservoir porosity and permeability have been mapped and analyzed. Two favorable zones have been suggested for further research in the survey.

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