scholarly journals Numerical Modeling of Suspension Force for Bearingless Flywheel Machine Based on Differential Evolution Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4470
Author(s):  
Zhu ◽  
Zhu ◽  
Guo ◽  
Jiang ◽  
Sun
Keyword(s):  
Numerical Modeling ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Mean Squared Error ◽  
Modeling Method ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Prediction Errors ◽  
Element Analysis ◽  
Learning Machine

The analytical model (AM) of suspension force in a bearingless flywheel machine has model mismatch problems due to magnetic saturation and rotor eccentricity. A numerical modeling method based on the differential evolution (DE) extreme learning machine (ELM) is proposed in this paper. The representative input and output sample set are obtained by finite-element analysis (FEA) and principal component analysis (PCA), and the numerical model of suspension force is obtained by training ELM. Additionally, the DE algorithm is employed to optimize the ELM parameters to improve the model accuracy. Finally, absolute error (AE) and root mean squared error (RMSE) are introduced as evaluation indexes to conduct comparative analyses with other commonly-used machine learning algorithms, such as k-Nearest Neighbor (KNN), the back propagation (BP) algorithm, and support vector machines (SVMs). The results show that, compared with the above algorithm, the proposed method has smaller fitting and prediction errors; the RMSE value is just 22.88% of KNN, 39.90% of BP, and 58.37% of SVM, which verifies the effectiveness and validity of the proposed numerical modeling method.

scholarly journals Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1148 ◽  
Author(s):  
Rei Sonobe
Keyword(s):  
Extreme Learning Machine ◽  
Local Governments ◽  
Multiple Kernel Learning ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Multiple Kernel ◽  
Crop Classification ◽  
Learning Machine ◽  
Simple Ratio

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

scholarly journals A Novel Machine Learning Based Method of Combined Dynamic Environment Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Wentao Mao ◽  
Guirong Yan ◽  
Longlei Dong
Keyword(s):  
Machine Learning ◽  
Multiple Input Multiple Output ◽  
Dynamic Environment ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Structural Vibration ◽  
Support Vector ◽  
Prediction Errors ◽  
Vibration Responses ◽  
Learning Machine

In practical engineerings, structures are often excited by different kinds of loads at the same time. How to effectively analyze and simulate this kind of dynamic environment of structure, named combined dynamic environment, is one of the key issues. In this paper, a novel prediction method of combined dynamic environment is proposed from the perspective of data analysis. First, the existence of dynamic similarity between vibration responses of the same structure under different boundary conditions is theoretically proven. It is further proven that this similarity can be established by a multiple-input multiple-output regression model. Second, two machine learning algorithms, multiple-dimensional support vector machine and extreme learning machine, are introduced to establish this model. To test the effectiveness of this method, shock and stochastic white noise excitations are acted on a cylindrical shell with two clamps to simulate different dynamic environments. The prediction errors on various measuring points are all less than ±3 dB, which shows that the proposed method can predict the structural vibration response under one boundary condition by means of the response under another condition in terms of precision and numerical stability.

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.

A novel hybrid model using the rotation forest-based differential evolution online sequential extreme learning machine for illumination correction of dyed fabrics

2018 ◽  
Vol 89 (7) ◽  
pp. 1180-1197 ◽  
Author(s):  
Zhiyu Zhou ◽  
Xu Gao ◽  
Jianxin Zhang ◽  
Zefei Zhu ◽  
Xudong Hu
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Differential Evolution Algorithm ◽  
Image Features ◽  
Support Vector ◽  
Rotation Forest ◽  
Generalization Ability ◽  
Learning Machine ◽  
Hidden Layer

This study proposes an ensemble differential evolution online sequential extreme learning machine (DE-OSELM) for textile image illumination correction based on the rotation forest framework. The DE-OSELM solves the inaccuracy and long training time problems associated with traditional illumination correction algorithms. First, the Grey–Edge framework is used to extract the low-dimensional and efficient image features as online sequential extreme learning machine (OSELM) input vectors to improve the training and learning speed of the OSELM. Since the input weight and hidden-layer bias of OSELMs are randomly obtained, the OSELM algorithm has poor prediction accuracy and low robustness. To overcome this shortcoming, a differential evolution algorithm that has the advantages of good global search ability and robustness is used to optimize the input weight and hidden-layer bias of the DE-OSELM. To further improve the generalization ability and robustness of the illumination correction model, the rotation forest algorithm is used as the ensemble framework, and the DE-OSELM is used as the base learner to replace the regression tree algorithm in the original rotation forest algorithm. Then, the obtained multiple different DE-OSELM learners are aggregated to establish the prediction model. The experimental results show that compared with the textile color correction algorithm based on the support vector regression and extreme learning machine algorithms, the ensemble illumination correction method achieves high prediction accuracy, strong robustness, and good generalization ability.

scholarly journals Predicting China's Economic Running State Using Machine Learning

2018 ◽  
Vol 232 ◽  
pp. 03036
Author(s):  
Jianxiang Luo ◽  
Yonggang Fu
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Extreme Learning Machine ◽  
Early Warning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Daunting Task ◽  
Coincidence Index ◽  
Learning Machine

China's business index of macro-economic includes early warning index, coincidence index, leading index and lagging index, among which early warning index reflects the economic running state. However, obtaining these indexes is a complex and daunting task. To simplify the task, this article mainly explores how to use machine learning algorithms including multiple linear regression (MLR), support vector machine regression (SVM), random forest (RF), artificial neural network (ANN) and extreme learning machine (ELM) to accurately predict early warning index. Finally, it can be found that the warning index can be well predicted by above machine learning algorithms with coincidence index, leading index and lagging index to be variables, furthermore, extreme learning machine and random forest are superior to other methods.

scholarly journals An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zi-Ji Yan
Keyword(s):  
Ion Channel ◽  
Extreme Learning Machine ◽  
Nuclear Receptor ◽  
Drug Target ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Weighted Extreme Learning Machine ◽  
Speed Up ◽  
Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

scholarly journals Multi-Classification of Fetal Health Status Using Extreme Learning Machine

2021 ◽  
Vol 5 (2) ◽  
pp. 62-70
Author(s):  
Ömer KASIM
Keyword(s):  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Binary Classification ◽  
Clinical Decision ◽  
Support Vector ◽  
Data Set ◽  
Multiple Classification ◽  
Learning Machine ◽  
Multi Class Classification ◽  
The University

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive.  When multi-class classification was performed, the accuracy was obtained as 98.12%.  The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

scholarly journals Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Nan Liu ◽  
Jiuwen Cao ◽  
Zhi Xiong Koh ◽  
Pin Pin Pek ◽  
Marcus Eng Hock Ong
Keyword(s):  
Emergency Department ◽  
Risk Stratification ◽  
Extreme Learning Machine ◽  
Tertiary Hospital ◽  
Support Vector ◽  
Characteristic Analysis ◽  
Scoring Method ◽  
Stratification Method ◽  
Emergency Department Patients ◽  
Learning Machine

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis.

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