scholarly journals Diagnosing Anorexia Based on Partial Least Squares, Back Propagation Neural Network, and Support Vector Machines

2004 ◽  
Vol 44 (6) ◽  
pp. 2040-2046 ◽  
Author(s):  
C. Y. Zhao ◽  
R. S. Zhang ◽  
H. X. Liu ◽  
C. X. Xue ◽  
S. G. Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Least Squares ◽  
Partial Least Squares ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Vector Machines

scholarly journals Dissolved Gases Forecasting Based on Wavelet Least Squares Support Vector Regression and Imperialist Competition Algorithm for Assessing Incipient Faults of Transformer Polymer Insulation

Polymers ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 85 ◽  
Author(s):  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Yiyi Zhang ◽  
Xin Li ◽  
Jiake Fang ◽  
...  
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Dissolved Gases ◽  
Dissolved Gas ◽  
Simulated Case

A solution for forecasting the dissolved gases in oil-immersed transformers has been proposed based on the wavelet technique and least squares support vector machine. In order to optimize the hyper-parameters of the constructed wavelet LS-SVM regression, the imperialist competition algorithm was then applied. In this study, the assessment of prediction performance is based on the squared correlation coefficient and mean absolute percentage error methods. According to the proposed method, this novel procedure was applied to a simulated case and the experimental results show that the dissolved gas contents could be accurately predicted using this method. Besides, the proposed approach was compared to other prediction methods such as the back propagation neural network, the radial basis function neural network, and generalized regression neural network. By comparison, it was inferred that this method is more effective than previous forecasting methods.

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