Kernel principal component analysis-based least squares support vector machine optimized by improved grey wolf optimization algorithm and application in dynamic liquid level forecasting of beam pump

2019 ◽  
Vol 42 (6) ◽  
pp. 1135-1150 ◽  
Author(s):  
Tian Zhongda
Keyword(s):  
Principal Component ◽  
Liquid Level ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Grey Wolf Optimization ◽  
Modeling Process ◽  
Parameters Selection ◽  
Forecasting Method ◽  
Beam Pump ◽  
Input Variables

Considering the blind parameters selection and the high dimension of input data in least squares support vector machine (LSSVM) modeling process, a kernel principal component analysis (KPCA)-based LSSVM forecasting method optimized by improved grey wolf optimization (GWO) algorithm is proposed. As an excellent forecasting model, the regression forecasting performance of LSSVM is greatly affected by parameters selection of the model. An improved GWO algorithm with better performance is proposed to determine the optimal parameters of LSSVM. This improved GWO algorithm improves the optimization precision and global optimization ability of the standard GWO algorithm. The parameters of LSSVM model are taken as the optimization object that is optimized by improved GWO algorithm. At the same time, the input variables of LSSVM are correlated and redundant. KPCA algorithm can eliminate the correlation and redundancy between input variables. The reduction of input variables reduces the complexity and training time of modeling process, and the coupling between input variables, to improve the prediction accuracy of LSSVM. The dynamic liquid level of beam pump is chosen as the research object. The proposed forecasting method is applied to the prediction of dynamic liquid level. The simulation comparison on actual collected dynamic liquid level data is performed. The simulation results show that the proposed forecasting method has better predictive performance for the dynamic liquid level.

scholarly journals Sensor Fault Diagnosis Method Based on α -Grey Wolf Optimization-Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xuezhen Cheng ◽  
Dafei Wang ◽  
Chuannuo Xu ◽  
Jiming Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Similar Data ◽  
Grey Wolf ◽  
Sensor Fault ◽  
Grey Wolf Optimization ◽  
Diagnosis Method

Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of α Grey Wolf Optimization Support Vector Machine (α-GWO-SVM) in this paper. Firstly, a fusion with Kernel Principal Component Analysis (KPCA) and time-domain parameters is performed to carry out the feature extraction and dimensionality reduction for fault data. Then, an improved Grey Wolf Optimization (GWO) algorithm is applied to enhance its global search capability while speeding up the convergence, for the purpose of further optimizing the parameters of SVM. Finally, the experimental results are obtained to suggest that the proposed method performs better in optimization than the other intelligent diagnosis algorithms based on SVM, which improves the accuracy of fault diagnosis effectively.

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

scholarly journals Research on Recognition Method of Driving Fatigue State Based on Sample Entropy and Kernel Principal Component Analysis

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 701 ◽  
Author(s):  
Beige Ye ◽  
Taorong Qiu ◽  
Xiaoming Bai ◽  
Ping Liu
Keyword(s):  
Principal Component Analysis ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Sample Entropy ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Recognition Method ◽  
State Recognition ◽  
Driving Fatigue

In view of the nonlinear characteristics of electroencephalography (EEG) signals collected in the driving fatigue state recognition research and the issue that the recognition accuracy of the driving fatigue state recognition method based on EEG is still unsatisfactory, this paper proposes a driving fatigue recognition method based on sample entropy (SE) and kernel principal component analysis (KPCA), which combines the advantage of the high recognition accuracy of sample entropy and the advantages of KPCA in dimensionality reduction for nonlinear principal components and the strong non-linear processing capability. By using support vector machine (SVM) classifier, the proposed method (called SE_KPCA) is tested on the EEG data, and compared with those based on fuzzy entropy (FE), combination entropy (CE), three kinds of entropies including SE, FE and CE that merged with KPCA. Experiment results show that the method is effective.

Stellar Parameterisation using KPCA and SVM

2017 ◽  
Vol 14 (S339) ◽  
pp. 345-348
Author(s):  
H. Yuan ◽  
Y. Zhang ◽  
Y. Lei ◽  
Y. Dong ◽  
Z. Bai ◽  
...  
Keyword(s):  
Effective Temperature ◽  
Hybrid Approach ◽  
Principal Component ◽  
Test Sample ◽  
Surface Gravity ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Stellar Spectra ◽  
The Impact ◽  
Temperature Surface

AbstractWith so many spectroscopic surveys, both past and upcoming, such as SDSS and LAMOST, the number of accessible stellar spectra is continuously increasing. There is therefore a great need for automated procedures that will derive estimates of stellar parameters. Working with spectra from SDSS and LAMOST, we put forward a hybrid approach of Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) to determine the stellar atmospheric parameters effective temperature, surface gravity and metallicity. For stars with both APOGEE and LAMOST spectra, we adopt the LAMOST spectra and APOGEE parameters, and then use KPCA to reduce dimensionality and SVM to measure parameters. Our method provides reliable and precise results; for example, the standard deviation of effective temperature, surface gravity and metallicity for the test sample come to approximately 47–75 K, 0.11–0.15 dex and 0.06–0.075 dex, respectively. The impact of the signal:noise ratio of the observations upon the accuracy of the results is also investigated.

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