Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation

Nonfiction, Science & Nature, Mathematics, Applied, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation by Daniela Sanchez, Patricia Melin, Springer International Publishing
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Author: Daniela Sanchez, Patricia Melin ISBN: 9783319288628
Publisher: Springer International Publishing Publication: February 23, 2016
Imprint: Springer Language: English
Author: Daniela Sanchez, Patricia Melin
ISBN: 9783319288628
Publisher: Springer International Publishing
Publication: February 23, 2016
Imprint: Springer
Language: English

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.

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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.

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