A wavelength selection method based on random decision particle swarm optimization with attractor for near-infrared spectral quantitative analysis

2015 ◽  
Vol 29 (5) ◽  
pp. 289-299 ◽  
Author(s):  
Hui Cao ◽  
Yanxia Wang ◽  
Sanchun Yang ◽  
Yan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Near Infrared ◽  
Particle Swarm ◽  
Selection Method ◽  
Wavelength Selection ◽  
Swarm Optimization ◽  
Infrared Spectral

scholarly journals Application of particle swarm optimization-based least square support vector machine in quantitative analysis of extraction solution of yangxinshi tablet using near infrared spectroscopy

2014 ◽  
Vol 07 (06) ◽  
pp. 1450011 ◽  
Author(s):  
Weijian Lou ◽  
Kai Yang ◽  
Miaoqin Zhu ◽  
Yongjiang Wu ◽  
Xuesong Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Particle Swarm ◽  
Least Square ◽  
Support Vector ◽  
Swarm Optimization ◽  
Calibration Models

A particle swarm optimization (PSO)-based least square support vector machine (LS-SVM) method was investigated for quantitative analysis of extraction solution of Yangxinshi tablet using near infrared (NIR) spectroscopy. The usable spectral region (5400–6200 cm-1) was identified, then the first derivative spectra smoothed using a Savitzky–Golay filter were employed to establish calibration models. The PSO algorithm was applied to select the LS-SVM hyperparameters (including the regularization and kernel parameters). The calibration models of total flavonoids, puerarin, salvianolic acid B and icariin were established using the optimum hyperparameters of LS-SVM. The performance of LS-SVM models were compared with partial least squares (PLS) regression, feed-forward back-propagation network (BPANN) and support vector machine (SVM). Experimental results showed that both the calibration results and prediction accuracy of the PSO-based LS-SVM method were superior to PLS, BP-ANN and SVM. For PSO-based LS-SVM models, the determination coefficients (R2) for the calibration set were above 0.9881, and the RSEP values were controlled within 5.772%. For the validation set, the RMSEP values were close to RMSEC and less than 0.042, the RSEP values were under 8.778%, which were much lower than the PLS, BP-ANN and SVM models. The PSO-based LS-SVM algorithm employed in this study exhibited excellent calibration performance and prediction accuracy, which has definite practice significance and application value.

Quantitative Analysis for SF6 and its Compositions in GIS

2012 ◽  
Vol 562-564 ◽  
pp. 1336-1339
Author(s):  
Hai Lun Wang ◽  
Jian Wei Shen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Fault Diagnosis ◽  
Traditional Method ◽  
Particle Swarm ◽  
Prediction Errors ◽  
Swarm Optimization ◽  
Training Analysis ◽  
Derivatives Of

In this paper, a method for GIS equipment fault diagnosis by the analysis of volume fractions of the derivatives of SF6 gas inside GIS equipment is presented. For the method, based on the differential spectra method, a neural network model and the particle swarm optimization are used for training analysis of infrared spectra, to realize the quantitative analysis of specific derivatives. The experimental results show that the prediction errors obtained by particle swarm optimization training are markedly superior to prediction errors obtained using the traditional method.

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