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

Qian Wang,Yajie Yu,Hui Cao,Boyan Cai

doi:10.1155/2016/5616503

Abstract

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.

Highlights

Spectral quantitative analysis is a nondestructive and fast measurement technique and has been used in a variety of chemical fields [1,2,3]
Backpropagation neural network (BPNN) is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of BPNN is realized by the nonlinear adaptive evolutionary programming (NAEP)
For GA-Partial least squares (PLS), a random population including a number of chromosomes is initialized, and the PLS model is built for each chromosome, where each chromosome represents a solution of wavelength selection

Summary

Introduction

Spectral quantitative analysis is a nondestructive and fast measurement technique and has been used in a variety of chemical fields [1,2,3]. Based on the obtained wavelength signals, the spectral quantitative analysis model is built to predict the component concentrations by the regression algorithms [5]. A spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. BPNN is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of BPNN is realized by the nonlinear adaptive evolutionary programming (NAEP). The hybrid chromosome in binary scheme of NAEP has four fragments, which represent the number of the hidden layers of BPNN, the number of neurons in each hidden layer, the selection of spectral wavelengths, and two adaptive parameters of the mutation probability of NAEP, respectively. The chromosome with lowest fitness value is the final result; namely, the selected wavelength and the corresponding topology structure of BPNN are determined.

The Related Methods

The Proposed Method

Experimental Datasets

Conclusions