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Neural Network Approaches to Solution of the Inverse Problem of Identification and Determination of the Ionic Composition of Multi-component Water Solutions

  • Sergey DolenkoEmail author
  • Alexander Efitorov
  • Sergey Burikov
  • Tatiana Dolenko
  • Kirill Laptinskiy
  • Igor Persiantsev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)

Abstract

The studied inverse problem is determination of ionic composition of inorganic salts (concentrations of up to 10 ions) in multi-component water solutions by their Raman spectra. The regression problem was solved in two ways: 1) by a multilayer perceptron trained on the large dataset, composed of spectra of all possible mixing options of ions in water; 2) dividing the data set into compact clusters and creating regression models for each cluster separately. Within the first approach, we used supervised training of neural network, achieving good results. Unfortunately, this method isn’t stable enough; the results depend on data subdivision into training, test, and out-of-sample sets. In the second approach, we used algorithms of unsupervised learning for data clustering: Kohonen networks, k-means, k-medoids and hierarchical clustering, and built partial least squares regression models on the small datasets of each cluster. Both approaches and their results are discussed in this paper.

Keywords

Inverse problems Multi-component solutions Ionic composition Identification Clusterization Raman spectroscopy 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sergey Dolenko
    • 1
    Email author
  • Alexander Efitorov
    • 1
    • 2
  • Sergey Burikov
    • 1
    • 2
  • Tatiana Dolenko
    • 1
    • 2
  • Kirill Laptinskiy
    • 1
    • 2
  • Igor Persiantsev
    • 1
  1. 1.D.V. Skobeltsyn Institute of Nuclear PhysicsM.V. Lomonosov Moscow State UniversityMoscowRussia
  2. 2.Physical DepartmentM.V. Lomonosov State UniversityMoscowRussia

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