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Quantitative spectral data analysis using extreme learning machines algorithm incorporated with PCA

journal contribution
posted on 19.04.2021, 04:21 by Minmei LiMinmei Li, Santoso WibowoSantoso Wibowo, Wei LiWei Li, Dujuan LiDujuan Li
Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system.

History

Volume

14

Issue

1

Start Page

1

End Page

14

Number of Pages

14

eISSN

1999-4893

Publisher

MDPI AG

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

07/01/2021

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

Algorithms

Article Number

18