Melt index prediction by weighted least squares support vector machines

2006 ◽  
Vol 101 (1) ◽  
pp. 285-289 ◽  
Author(s):  
Jian Shi ◽  
Xinggao Liu
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Melt Index ◽  
Vector Machines

scholarly journals An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yijun Chen ◽  
Chongshi Gu ◽  
Chenfei Shao ◽  
Hao Gu ◽  
Dongjian Zheng ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Deformation Prediction ◽  
Ant Lion Optimizer ◽  
Proposed Model ◽  
Vector Machines ◽  
Ant Lion ◽  
Dam Deformation

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

Rapid determination of phytic acid content in cottonseed meal via near infrared spectroscopy

2017 ◽  
Vol 25 (3) ◽  
pp. 188-195 ◽  
Author(s):  
Rubing Zhao ◽  
Xiaojian Xu ◽  
Jiale Li ◽  
Cheng Li ◽  
Jinhong Chen ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Phytic Acid ◽  
Near Infrared ◽  
Rapid Determination ◽  
Weighted Least Squares ◽  
Support Vector ◽  
Calibration Model ◽  
Phytic Acid Content ◽  
Vector Machines

A near infrared calibration model with higher precision and better stability was constructed in the present study, using 280 cottonseed samples. The reference phytic acid contents were determined by high-performance ion chromatography. A combination of Savitzky–Golay smoothing, standard normal variate, and the first derivative was chosen as the spectral pre-treatment method. Monte Carlo uninformative variable elimination was proposed for spectral variable selection. The regression methods of partial least squares, least squares support vector machines, and weighted least squares support vector machines were developed for the calibration model. The optimal near infrared calibration model for phytic acid contents in the cottonseed meals was least squares support vector machines, with r2 = 0.97, RPD = 5.53, RMSECV = 0.06%, and RMSEP = 0.05%. This robust method can replace the traditional method of phytic acid determination in cottonseed meals.

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