scholarly journals Separation and Determination of Honokiol and Magnolol in Chinese Traditional Medicines by Capillary Electrophoresis with the Application of Response Surface Methodology and Radial Basis Function Neural Network

2011 ◽  
Vol 50 (1) ◽  
pp. 71-75 ◽  
Author(s):  
P. Han ◽  
F. Luan ◽  
X. Yan ◽  
Y. Gao ◽  
H. Liu
Keyword(s):  
Neural Network ◽  
Response Surface Methodology ◽  
Capillary Electrophoresis ◽  
Radial Basis Function ◽  
Response Surface ◽  
Basis Function ◽  
Traditional Medicines ◽  
Radial Basis

scholarly journals Parametric Optimization of the Poly (Nvinylcaprolactam) (PNVCL) Thermoresponsive Polymers Synthesis by the Response Surface Methodology and Radial Basis Function neural network

2018 ◽  
Vol 225 ◽  
pp. 02023
Author(s):  
Marwah N. Mohammed ◽  
Kamal Bin Yusoh ◽  
Jun Haslinda Binti Haji Shariffuddin
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Response Surface Methodology ◽  
Radial Basis Function ◽  
Response Surface ◽  
Basis Function ◽  
Conversion Rate ◽  
Maximum Effect ◽  
Thermoresponsive Polymers ◽  
Radial Basis

A novel comparison study based on a radial basis function neural network (RBFNN) and Response Surface Methodology (RSM) is proposed to predict the conversion rate (yield) of the experimental data for PNVCL polymerization. A statistical and optimization model was performing to show the effect of each parameter and their interactions on the conversion rate. The influence of the time, polymerization temperature, initiator concentration and concentration of the monomer were studied. The results obtained in this study indicate that the RBFNN was an effective method for predicting the conversion rate. The time of the PNVCL polymerization as well as the concentration of the monomer show the maximum effect on the conversion rate. In addition, compared with the RSM method, the RBFNN showed better conversion rate comparing with the experimental data.

Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data

2000 ◽  
Vol 69 (3) ◽  
pp. 348-358 ◽  
Author(s):  
Phillip Evans ◽  
Krishna C Persaud ◽  
Alexander S McNeish ◽  
Robert W Sneath ◽  
Norris Hobson ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Electronic Nose ◽  
Basis Function ◽  
Wheat Quality ◽  
Radial Basis

Three Modeling Methods Application in Near-Infrared Spectra Analysis for Determination of Volatile in Lignite Coal Samples

2014 ◽  
Vol 541-542 ◽  
pp. 1438-1441
Author(s):  
Xiao Li Yang ◽  
Fan Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Near Infrared ◽  
Partial Least Square ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Radial Basis ◽  
Lignite Coal

We studied volatile determination in lignite coal samples using near-infrared (NIR) spectra. Firstly, spectra were pre-processed to eliminate useless information. Then, determination model was constructed by partial least squares regression. We used discrete wavelet transform to pre-processing. To study the influence of modeling on determination of volatile for NIR analysis of lignite coal samples, we applied three techniques to build determination model, including support vector regression, partial least square regression and radial basis function neural network. Comparison of the mean absolute percentage error (MAPE) and root mean square error of prediction (RMSEP) of the models show that the models constructed with radial basis function neural network gave the best results.

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