Solar wind data analysis using self-organizing hierarchical neural network classifiers

Dolenko, S.A.; Orlov, Y.V.; Persiantsev, I.G.; Shugai, J.S.; Dmitriev, A.V.; Suvorova, A.V.; Veselovsky, I.S.

Авторы: Dolenko S.A., Orlov Y.V., Persiantsev I.G., Shugai J.S., Dmitriev A.V., Suvorova A.V., Veselovsky I.S.
Сборник: Multiple Classifier Systems. Second International Workshop, MCS 2001, Cambridge, UK, July 2001, proceedings
Серия: Lecture Notes in Computer Science
Том: 2096
Год издания: 2001
Место издания: Springer-Verlag Berlin Heidelberg
Первая страница: 289
Последняя страница: 298
DOI: 10.1007/3-540-48219-9_29
Аннотация: Recently, we have proposed an algorithm for construction of a hierarchy of neural network classifiers based on a modification of error backpropagation. It combines supervised learning with self-organization. Recursive use of the algorithm results in creation of compact and computationally effective self-organized structures of neural classifiers. The algorithm is applicable for unsupervised analysis of both static objects and dynamic objects, described by time series. In the latter case, the algorithm performs segmentation of the analyzed time-series into parts characterized by different types of dynamics. The algorithm has been successfully tested on pseudo-chaotic maps. In this paper the above algorithm is applied to Solar wind data analysis. Preliminary results indicate that new structural classes in the Solar wind could be distinguished aside from the traditional two- and three-state concepts.
Добавил в систему: Доленко Сергей Анатольевич

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Solar wind data analysis using self-organizing hierarchical neural network classifiersстатья Исследовательская статья