Robust least squares support vector machine algorithm for mobile location

2009 ◽  
Author(s):  
J.Y. Huang ◽  
P. Wang ◽  
Q. Wan ◽  
L.P. Chiang ◽  
F.H. Choo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Mobile Location ◽  
Robust Least Squares

Modeling of Flexible Structures by Means of Least Square Support Vector Machine

2015 ◽  
Author(s):  
Hammam Tamimi ◽  
Dirk Söffker
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Flexible Structures ◽  
Operating Conditions ◽  
Least Square ◽  
Support Vector ◽  
Suitable Model ◽  
Support Vector Machine Algorithm ◽  
Training Time ◽  
Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

scholarly journals Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3108 ◽  
Author(s):  
Chu Zhang ◽  
Chaoshun Li ◽  
Tian Peng ◽  
Xin Xia ◽  
Xiaoming Xue ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Search Algorithm ◽  
Support Vector ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Governing System ◽  
Robust Least Squares ◽  
Pumped Storage ◽  
Identification Model

In view of the complex and changeable operating environment of pumped storage power stations and the noise and outliers in the modeling data, this study proposes a sparse robust least squares support vector machine (LSSVM) model based on the hybrid backtracking search algorithm for the model identification of a pumped turbine governing system. By introducing the maximum linearly independent set, the sparsity of the support vectors of the LSSVM model are realized, and the complexity is reduced. The robustness of the identification model to noise and outliers is enhanced using the weighted function based on improved normal distribution. In order to further improve the accuracy and generalization performance of the sparse robust LSSVM identification model, the model input variables, the kernel parameters, and the regularization parameters are optimized synchronously using a binary-real coded backtracking search algorithm. Experiments on two benchmark problems and a real-world application of a pumped turbine governing system in a pumped storage power station in China show that the proposed sparse robust LSSVM model optimized by the hybrid backtracking search algorithm can not only obtain higher identification accuracy, it also has better robustness and a higher generalization performance compared with the other existing models.

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