logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Hierarchical Modular Granular Neural Networks With Fuzzy Aggregation 1st Edition Daniela Sanchez

  • SKU: BELL-5605106
Hierarchical Modular Granular Neural Networks With Fuzzy Aggregation 1st Edition Daniela Sanchez
$ 31.00 $ 45.00 (-31%)

4.3

58 reviews

Hierarchical Modular Granular Neural Networks With Fuzzy Aggregation 1st Edition Daniela Sanchez instant download after payment.

Publisher: Springer International Publishing
File Extension: PDF
File size: 2.89 MB
Pages: 107
Author: Daniela Sanchez, Patricia Melin (auth.)
ISBN: 9783319288611, 9783319288628, 331928861X, 3319288628
Language: English
Year: 2016
Edition: 1

Product desciption

Hierarchical Modular Granular Neural Networks With Fuzzy Aggregation 1st Edition Daniela Sanchez by Daniela Sanchez, Patricia Melin (auth.) 9783319288611, 9783319288628, 331928861X, 3319288628 instant download after payment.

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.

Related Products