Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization
Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2016/5616503 |
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| _version_ | 1832562649153404928 |
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| author | Qian Wang Boyan Cai Yajie Yu Hui Cao |
| author_facet | Qian Wang Boyan Cai Yajie Yu Hui Cao |
| author_sort | Qian Wang |
| collection | DOAJ |
| description | Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP). The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool. |
| format | Article |
| id | doaj-art-0301128782c2478f87a306bc50bf2351 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-0301128782c2478f87a306bc50bf23512025-02-03T01:22:00ZengWileyJournal of Spectroscopy2314-49202314-49392016-01-01201610.1155/2016/56165035616503Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure OptimizationQian Wang0Boyan Cai1Yajie Yu2Hui Cao3School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, ChinaShaanxi Key Laboratory of Smart Grid and State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaShaanxi Key Laboratory of Smart Grid and State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaShaanxi Key Laboratory of Smart Grid and State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, ChinaSpectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP). The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool.http://dx.doi.org/10.1155/2016/5616503 |
| spellingShingle | Qian Wang Boyan Cai Yajie Yu Hui Cao Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization Journal of Spectroscopy |
| title | Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization |
| title_full | Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization |
| title_fullStr | Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization |
| title_full_unstemmed | Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization |
| title_short | Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization |
| title_sort | spectral quantitative analysis model with combining wavelength selection and topology structure optimization |
| url | http://dx.doi.org/10.1155/2016/5616503 |
| work_keys_str_mv | AT qianwang spectralquantitativeanalysismodelwithcombiningwavelengthselectionandtopologystructureoptimization AT boyancai spectralquantitativeanalysismodelwithcombiningwavelengthselectionandtopologystructureoptimization AT yajieyu spectralquantitativeanalysismodelwithcombiningwavelengthselectionandtopologystructureoptimization AT huicao spectralquantitativeanalysismodelwithcombiningwavelengthselectionandtopologystructureoptimization |