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Granular Computing
Erschienen in: Granular Computing 4/2016

01.12.2016 | Original Paper

A study of granular computing in the agenda of growth of artificial neural networks

verfasst von: Mingli Song, Yongbin Wang

Erschienen in: Granular Computing | Ausgabe 4/2016

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Abstract

Granular neural networks (GNN) are designed to process complex non-numerical data or the combination of numerical and non-numerical data. The concept of “granules” here refers to various data groups which are drawn together by the criteria of similarity or functionality. Granular neural networks are being used in areas of knowledge discovery, pattern recognition, etc. This paper carries out a comprehensive review of articles that involve a comparative study of different types of granular neural networks and their application. This study aims to give useful insight into the capability of granular neural networks.
Vorheriger Artikel A neurofuzzy algorithm for learning from complex granules
Nächster Artikel Rule-based systems: a granular computing perspective

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Metadaten
Titel
A study of granular computing in the agenda of growth of artificial neural networks
verfasst von
Mingli Song
Yongbin Wang
Publikationsdatum
01.12.2016
Verlag
Springer International Publishing
Erschienen in
Granular Computing / Ausgabe 4/2016
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-016-0020-7