Use of Wavelet Neural Networks to Solve Inverse Problems in Spectroscopy of Multi-component Solutions

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.

[1]  I. G. Persiantsev,et al.  Adaptive methods for solving inverse problems in laser raman spectroscopy of multi-component solutions , 2012, Pattern Recognition and Image Analysis.

[2]  Alexander Efitorov,et al.  Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts by Artificial Neural Networks , 2016, ICANN.

[3]  Qinghua Zhang,et al.  Using wavelet network in nonparametric estimation , 1997, IEEE Trans. Neural Networks.

[4]  Qingmei Sui,et al.  A Stepwise Updating Algorithm for Multiresolution Wavelet Neural Networks , 2003, WAA.

[5]  Alexander Efitorov,et al.  Neural Network Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts , 2016 .

[6]  S. Burikov,et al.  The effect of hydration of ions of inorganic salts on the shape of the Raman stretching band of water , 2005 .

[7]  I. G. Persiantsev,et al.  Application of artificial neural networks to solve problems of identification and determination of concentration of salts in multi-component water solutions by Raman spectra , 2010, Optical Memory and Neural Networks.

[8]  Tatiana A. Dolenko,et al.  Valence band of liquid water Raman scattering: some peculiarities and applications in the diagnostics of water media , 2000 .

[9]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[10]  Jun Zhang,et al.  Wavelet neural networks for function learning , 1995, IEEE Trans. Signal Process..

[11]  Shu-Ching Chen,et al.  Function approximation using robust wavelet neural networks , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[12]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[13]  J. Saja,et al.  Effect of electrolyte concentration on the Raman spectra of water in aqueous solutions , 1986 .