Prediction of ozone hourly concentrations by support vector machine and kernel extreme learning machine using wavelet transformation and partial least squares methods

This paper presents the pattern classification of the binary microarray gene expression based medical data using extreme learning machine (ELM) and its variants like on-line sequential ELM (OSELM) and kernel based extreme learning machine (KELM). In the KELM category two variants namely the wavelet based kernel (WKELM) extreme learning machine and radial basis kernel extreme learning machine (RKELM) along with support vector machine (SVMRBF) and support vector machine polynomial (SVMPoly) are used to classify microarray medical datasets. Further to reduce the high dimensionality of Microarray medical datasets giving rise to high number of gene expression and small sample sizes, a modified evolutionary cat swarm optimization (MCSO) technique is adopted. The efficiency of the proposed algorithm is verified using a set of performance metrics for four binary medical datasets belonging to breast cancer, prostate cancer, colon tumor, and leukemia, respectively.

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A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

Mathematical Problems in Engineering ◽

10.1155/2014/906732 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 9

Author(s):

Qingsong Xu

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Extreme Learning Machine ◽

Learning Algorithm ◽

Support Vector ◽

Comparison Study ◽

Fast Learning ◽

Structural Impact ◽

Learning Machine ◽

Impact Localization

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have been developed for the location prediction. Comparative studies of the basic ELM, kernel-based ELM, and LSSVM models are carried out. Results show that the kernel-based ELM requires the shortest learning time and it is capable of producing suboptimal localization accuracy among the three models. Hence, ELM paves a promising way in structural impact detection.

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A Comparative Study of Extreme Learning Machine, Least Squares Support Vector Machine, Back Propagation Neural Network for Outlet Total Phosphorus Prediction

2018 Prognostics and System Health Management Conference (PHM-Chongqing) ◽

10.1109/phm-chongqing.2018.00128 ◽

2018 ◽

Author(s):

Tingting Yu ◽

Yun Bai

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Comparative Study ◽

Least Squares ◽

Extreme Learning Machine ◽

Total Phosphorus ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Support Vector ◽

Learning Machine

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A Hybrid Kernel Extreme Learning Machine and Improved Cat Swarm Optimization for Microarray Medical Data Classification

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2016070104 ◽

2016 ◽

Vol 7 (3) ◽

pp. 71-100 ◽

Cited By ~ 1

Author(s):

S. Chakravarty ◽

R. Bisoi ◽

P. K. Dash

Keyword(s):

Gene Expression ◽

Support Vector Machine ◽

Extreme Learning Machine ◽

Medical Data ◽

Small Sample ◽

Support Vector ◽

Swarm Optimization ◽

Cat Swarm Optimization ◽

Kernel Extreme Learning Machine ◽

Learning Machine

This paper presents the pattern classification of the binary microarray gene expression based medical data using extreme learning machine (ELM) and its variants like on-line sequential ELM (OSELM) and kernel based extreme learning machine (KELM). In the KELM category two variants namely the wavelet based kernel (WKELM) extreme learning machine and radial basis kernel extreme learning machine (RKELM) along with support vector machine (SVMRBF) and support vector machine polynomial (SVMPoly) are used to classify microarray medical datasets. Further to reduce the high dimensionality of Microarray medical datasets giving rise to high number of gene expression and small sample sizes, a modified evolutionary cat swarm optimization (MCSO) technique is adopted. The efficiency of the proposed algorithm is verified using a set of performance metrics for four binary medical datasets belonging to breast cancer, prostate cancer, colon tumor, and leukemia, respectively.

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Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Complexity ◽

10.1155/2020/6457517 ◽

2020 ◽

Vol 2020 ◽

pp. 1-26 ◽

Cited By ~ 1

Author(s):

Wei Xie ◽

Jie-sheng Wang ◽

Cheng Xing ◽

Sha-Sha Guo ◽

Meng-wei Guo ◽

...

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Extreme Learning Machine ◽

Prediction Accuracy ◽

Soft Sensor ◽

Support Vector ◽

Structure Parameters ◽

Generalization Ability ◽

Sensor Model ◽

Learning Machine

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

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Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine

Advances in Mechanical Engineering ◽

10.1177/1687814017751446 ◽

2018 ◽

Vol 10 (1) ◽

pp. 168781401775144 ◽

Cited By ~ 6

Author(s):

Jun Ma ◽

Jiande Wu ◽

Xiaodong Wang

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Extreme Learning Machine ◽

Wavelet Packet ◽

Support Vector ◽

Machine Method ◽

Energy Entropy ◽

Diagnosis Method ◽

Kernel Extreme Learning Machine ◽

Learning Machine

Aiming at connatural limitations of extreme learning machine in practice, a new fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine is proposed. On one hand, the presented method can extract the more efficient features using the wavelet packet-energy entropy method, and on the other hand, the sample fuzzy membership degree matrix U, weight matrix W which is used to describe the sample imbalance, and the kernel function are introduced to construct the fuzzy kernel extreme learning machine model with high accuracy and reliability. The experimental results of rolling bearing and check valve are obtained and analyzed in MATLAB 2010b. The results show that the proposed fuzzy kernel extreme learning machine method can obtain fairly or slightly better classification performance than the traditional extreme learning machine, kernel extreme learning machine, back propagation, support vector machine, and fuzzy support vector machine.

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

Mathematical Problems in Engineering ◽

10.1155/2015/542182 ◽

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.

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Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Mathematics ◽

10.3390/math8020152 ◽

2020 ◽

Vol 8 (2) ◽

pp. 152 ◽

Cited By ~ 2

Author(s):

Su-qi Zhang ◽

Kuo-Ping Lin

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Extreme Learning Machine ◽

Support Vector ◽

Local Optimum ◽

Short Term ◽

Swarm Optimization ◽

Traffic Flow Forecasting ◽

Learning Machine ◽

The One

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

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