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An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

Zhang, Peng, Zhou, Ji, Wang
2019 Applied Sciences  
Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network.  ...  In view of the difficulty in modeling the "S" characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning  ...  which makes it possible to apply the PLSR method by replacing the least square method.  ... 
doi:10.3390/app9193987 fatcat:usi6mpqqejgctk7uivnjv3ff3i

Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

Heng Liu, Caihong Zhang, Dongdong Huang
2017 Journal of Electrical and Computer Engineering  
In this work, for solar panel vision inspection, we present an extreme learning machine (ELM) and moving least square regression based approach to identify solder joint defect and detect the panel position  ...  Finally, moving least square regression (MLSR) algorithm is introduced for solar panel position determination.  ...  In practice, when we apply ELM for solder joint defect detection, we firstly extract the mentioned features [area, , , alpha, , ] from solder joint images and input them to ELM.  ... 
doi:10.1155/2017/7406568 fatcat:tt3yfmxygvb5pjsxydzm62kafy

Indirect measurement and extreme learning machine based modelling for flux linkage of doubly salient electromagnetic machine

Yanwu Xu, Zhuoran Zhang, Li Yu, Zhangming Bian
2018 IET electric power applications  
The ELM is employed to high-precision flux linkage modelling with high efficiency. A threephase 12/8-pole DSEM is tested to confirm the validity of the proposed modelling method.  ...  This study is aimed to demonstrate the feasibility of indirect flux linkage measurement method, as well as the effectiveness of the extreme learning machine (ELM)-based flux linkage modelling method.  ...  ELM is introduced to solve the non-linear relationships among angle, torque, and speed in the advanced angle control scheme of the DSEM [15] .  ... 
doi:10.1049/iet-epa.2017.0685 fatcat:grjifzg34rb47koirugerumchi

LARSEN-ELM: Selective ensemble of extreme learning machines using LARS for blended data

Bo Han, Bo He, Rui Nian, Mengmeng Ma, Shujing Zhang, Minghui Li, Amaury Lendasse
2015 Neurocomputing  
In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS).  ...  In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach.  ...  Our approach consists of two stages: First we employ least angle regression (LARS) to select the targeted inputs highly related to the outputs [21] .  ... 
doi:10.1016/j.neucom.2014.01.069 fatcat:5asljgrqnjexhl6hueedrtofpm

Terrain Referenced Navigation Using a Multilayer Radial Basis Function-Based Extreme Learning Machine

Jungshin Lee, Changky Sung, Juhyun Oh
2019 International Journal of Aerospace Engineering  
In this study, a high-precision terrain regression model to fit the DEM is generated using the extreme learning machine technique based on the multilayer radial basis function.  ...  It is difficult to secure such large memory spaces in small, low-priced unmanned aerial vehicles.  ...  The edited sample data: "TestDBData3.dat," "TrainDBData3.dat" in "TrainDB_ML_RBF_ELM_Using AE2_180919" Folder MRBF-ELM fitting S/W: "TrainDB_ ML_RBF_ELM_Using AE2_180919" Folder the simulation result data  ... 
doi:10.1155/2019/9142694 fatcat:ofcgjotyuzhtnhpi3zev6e5vhy

Long-term prediction of time series using NNE-based projection and OP-ELM

Antti Sorjamaa, Yoan Miche, Robert Weiss, Amaury Lendasse
2008 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)  
Then, after the network is first created using the original ELM, the selection of the most relevant nodes is performed by using a Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out  ...  estimation of the performances, leading to an Optimally-Pruned ELM (OP-ELM).  ...  Least Angle Regression (LARS) The LARS algorithm was proposed by Efron et al. in [7] and implemented in [17] .  ... 
doi:10.1109/ijcnn.2008.4634173 dblp:conf/ijcnn/SorjamaaMWL08 fatcat:3kaew5e42nb4jfqnwiqjbnphpu

ELM divertor peak energy fluence scaling to ITER with data from JET, MAST and ASDEX upgrade

T. Eich, B. Sieglin, A.J. Thornton, M. Faitsch, A. Kirk, A. Herrmann, W. Suttrop
2017 Nuclear Materials and Energy  
The extrapolated ELM energy fluencies are compared to material limits in ITER and found to be of concern.  ...  The so far analysed power load database for ELM mitigation experiments from JET-EFCC and Kicks, MAST-RMP and AUG-RMP operation are found to be consistent with both the scaling and the introduced model,  ...  Empirical scaling of the ELM energy fluence We apply standard least square fitting techniques to derive a regression law for the parallel ELM energy fluence.  ... 
doi:10.1016/j.nme.2017.04.014 fatcat:svzcqbe3fzfqziqs6kss564qay

An Advanced Pruning Method in the Architecture of Extreme Learning Machines Using L1-Regularization and Bootstrapping

Paulo Vitor de Campos Souza, Luiz Carlos Bambirra Torres, Gustavo Rodrigues Lacerda Silva, Antonio de Padua Braga, Edwin Lughofer
2020 Electronics  
Extreme learning machines (ELMs) are efficient for classification, regression, and time series prediction, as well as being a clear solution to backpropagation structures to determine values in intermediate  ...  Finally, pattern classification tests and benchmark regression tests of complex real-world problems are performed by comparing the proposed approach to other pruning models for ELMs.  ...  regularized least squares regression method is used that applies penalties to the coefficient vector.  ... 
doi:10.3390/electronics9050811 fatcat:tmqworhvgzclrpuj3qv7z2nkam

A Novel Improved ELM Algorithm for a Real Industrial Application

Hai-Gang Zhang, Sen Zhang, Yi-Xin Yin
2014 Mathematical Problems in Engineering  
In this paper, a new improved ELM algorithm named R-ELM is proposed to handle the multicollinear problem appearing in calculation of the ELM algorithm.  ...  Nowadays ELM algorithm has received wide application with its good generalization performance under fast learning speed. However, there are still several problems needed to be solved in ELM.  ...  Like the theory of Ridge Regression to overcome the multicollinear problem in least square method, we call our improved algorithm R-ELM. 2.2. The Improved R-ELM Algorithm.  ... 
doi:10.1155/2014/824765 fatcat:engicvexi5g2znplewohzoq24i

OP-ELM: Optimally Pruned Extreme Learning Machine

Yoan Miche, A. Sorjamaa, P. Bas, O. Simula, C. Jutten, A. Lendasse
2010 IEEE Transactions on Neural Networks  
The whole methodology is presented in detail and then applied to several regression and classification problems.  ...  Despite the simplicity and fast performance, the OP-ELM is still able to maintain an accuracy that is comparable to the performance of the SVM. A toolbox for the OP-ELM is publicly available online.  ...  It can be noted that the MRSR is mainly an extension of the least angle regression (LARS) algorithm [14] and hence, it is actually a variable ranking technique, rather than a selection one.  ... 
doi:10.1109/tnn.2009.2036259 pmid:20007026 fatcat:w24kzkmqi5arpefetkp7viwzdu

Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques

Mohammed Amin Benbouras, Alexandru-Ionuţ Petrişor, Hamma Zedira, Laala Ghelani, Lina Lefilef
2021 Applied Sciences  
This elaborated model provided the optimal prediction, i.e., the closest to the experimental values, compared to the other models and formulae proposed by previous studies.  ...  Finally, a reliable and easy-to-use graphical interface was generated, namely "BeaCa2021".  ...  , LASSO regression (LASSO), Random Forest (RF), Ridge Regression (Ridge), Partial Least Square Regression (PLSR), Stepwise Regression (Stepwise), Kernel Ridge (KRidge), Genetic Programming (GP), and Least  ... 
doi:10.3390/app112210908 fatcat:2l6x7vrs7fgj5f6s7obxrlm7vq

Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle

Han Guo, Jun Zhou, Fei Liu, Yong He, He Huang, Hongyan Wang
2020 Applied Sciences  
In this paper, four machine learning methods (REGRESS, least squares support vector machines (LS-SVM), extreme learning machine, and radial basis function neural network (RBFNN)) were applied for quantitatively  ...  Therefore, it is necessary to propose an evaluating method for a specific UAV spray nozzles.  ...  Machine Learning Methods Regress function achieved by MATLAB is an orthogonal least squares method for multiple linear regression; it has been applied in the fields of meteorology, economics [33] [34]  ... 
doi:10.3390/app10051759 fatcat:d3vb62ilxradxe2xhkdxjwthhi

TROP-ELM: A double-regularized ELM using LARS and Tikhonov regularization

Yoan Miche, Mark van Heeswijk, Patrick Bas, Olli Simula, Amaury Lendasse
2011 Neurocomputing  
In this paper an improvement of the optimally pruned extreme learning machine (OP-ELM) in the form of a L 2 regularization penalty applied within the OP-ELM is proposed.  ...  The proposed modification of the OP-ELM uses a cascade of two regularization penalties: first a L 1 penalty to rank the neurons of the hidden layer, followed by a L 2 penalty on the regression weights  ...  First comes a ranking of the neurons by a least angle regression (LARS [6] ; in practice the MRSR [29] implementation of LARS is used for it also applies to multi-output Illustration of the ELM model  ... 
doi:10.1016/j.neucom.2010.12.042 fatcat:jvtvugjuqjbszmzhgleg6batxu

Precise Burden Charging Operation during Iron-making Process in Blast Furnace

Haigang Zhang, Shaolun Sun, Sen Zhang
2021 IEEE Access  
In our work, by adaptively adjusting the opening degree of the throttle valve, it is possible to control accurate burden volume during the rotation of the charging chute, which can make sure to spill the  ...  The second problem refers to establishing a suitable burden charging strategy based on the basic burden surface and the optimal burden surface.  ...  ELM algorithm is applied to establish the relationship model between the burden surface and the production indexes.  ... 
doi:10.1109/access.2021.3064885 fatcat:3okbzrra3bakhfaje7ziudyuk4

Factors affecting branch failures in open-grown trees during a snowstorm in Massachusetts, USA

Brian Kane, John T Finn
2014 SpringerPlus  
We used logistic regression to assess whether the probability of branch failure differed among species, diameter at breast height (DBH) and the presence of a defect or leaves increased for different species  ...  The relationship between branch failure and DBH appeared to be due to the correlation between DBH and branch morphology, which was mostly similar among species.  ...  We used ordinary least squares (OLS) regression to determine if wood modulus of rupture (Kretschmann 2010) was associated with the probability of failure among species.  ... 
doi:10.1186/2193-1801-3-720 pmid:25674460 pmcid:PMC4320161 fatcat:ezy4lhat65g4bdgnpcw63zzsmu
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