| 光谱学与光谱分析 |
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| Simultaneous Spectrophotometric Determination of Multicomponent Mixtures by a Soft Thresholding Wavelet-based Radial Basis Function Neural Network |
| GAO Ling, REN Shou-xin |
| College of Chemistry and Chemical Engineering, Inner Mongolia University, Huhhot 010021, China |
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Abstract 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 σ 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.
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Received: 2003-01-26
Accepted: 2003-07-02
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Corresponding Authors:
REN Shou-xin
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| Cite this article: |
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GAO Ling,REN Shou-xin. Simultaneous Spectrophotometric Determination of Multicomponent Mixtures by a Soft Thresholding Wavelet-based Radial Basis Function Neural Network [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2004, 24(01): 106-110.
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| URL: |
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http://www.gpxygpfx.com/EN/Y2004/V24/I01/106 |
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