Use of wavelet neural networks to solve inverse problems in spectroscopy of multi-component solutions

Abstract

Wavelet neural networks (WNN) are a family of approximation algorithms that use wavelet functions to decompose the approximated function. They are more flexible than conventional multi-layer perceptrons (MLP), but they are more computationally expensive, and require more effort to find optimal parameters. In this study, we solve the inverse problems of determination of concentrations of components in multi-component solutions by their Raman spectra. The results demonstrated by WNN are compared to those obtained by MLP and by the linear partial least squares (PLS) method. It is shown that properly used WNN are a powerful method to solve multi-parameter inverse problems.