Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization

Abstract EN

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.