An Inverse Problem Involving Integration of Optical Spectroscopic Methods: Study of Influence of Feature Selection on Resilience of Neural Network Solution to Noise in Data

Авторы: Isaev Igor, Sarmanova Olga, Burikov Sergey, Dolenko Tatiana, Laptinskiy Kirill, Dolenko Sergey
Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research V
Серия: Studies in Computational Intelligence
Том: 1008
Год издания: 2022
Издательство: Springer International Publishing AG
Местоположение издательства: Cham, Switzerland
Первая страница: 344
Последняя страница: 351
DOI: 10.1007/978-3-030-91581-0_45
Аннотация: A neural network solution to the inverse problem of optical spectroscopy for determining ion concentrations in a multicomponent aqueous solution of heavy metal salts and methods for increasing its resilience to noise in the data are considered. It is shown that simultaneous use of the selection of significant input features and the integration of physical methods with the correct method of application allows one to achieve the best result.
Добавил в систему: Лаптинский Кирилл Андреевич

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