[Simultaneous spectrophotometric determination of multicomponent mixtures by a soft thresholding wavelet-based radial basis function neural network].
A Soft Thresholding Wavelet-based Radial Basis Function Neural network (STWRBFN) method was developed to perform simultaneous quantitative analysis of multicomponent mixtures. The quality of noise removal and regression was improved by combining wavelet soft thresholding with radial basis function neural network. Through optimization, the wavelet function, wavelet decomposition level (L), thresholding method and spread parameter sigma of RBFN were selected. Two-programs, i.e. PSTWRBFN and PRBFN, were designed to perform STWRBFN and RBFN calculations. Experimental results showed the STWRBFN method to be successful and better than RBFN. Comparing with classical multivariate linear regression, both the methods were more powerful.
Gao
Ling
L
College of Chemistry and Chemical Engineering, Inner Mongolia University, Huhhot 010021, China.
Ren
Shou-xin
SX
D000465:Algorithms; D001185:Artificial Intelligence; D002626:Chemistry, Pharmaceutical; D004867:Equipment Design; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D013053:Spectrophotometry / Q000379:methods; D013056:Spectrophotometry, Ultraviolet; D013598:Systems Theory