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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation/ by Daniela Sanchez, Patricia Melin.
Author:	Sanchez, Daniela.
other author:	Melin, Patricia.
Description:	VIII, 101 p. 57 illus., 50 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-28862-8
ISBN:	9783319288628

Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation
Sanchez, Daniela.

Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation[electronic resource] /by Daniela Sanchez, Patricia Melin. - 1st ed. 2016. - VIII, 101 p. 57 illus., 50 illus. in color.online resource. - SpringerBriefs in Computational Intelligence,2625-3704. - SpringerBriefs in Computational Intelligence,.

Introduction -- Background and Theory -- Proposed Method -- Application to Human Recognition -- Experimental Results -- Conclusions.

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

ISBN: 9783319288628

Standard No.: 10.1007/978-3-319-28862-8doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation
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