Details
Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation
SpringerBriefs in Applied Sciences and Technology
|
53,49 € |
|
| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 23.02.2016 |
| ISBN/EAN: | 9783319288628 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<p>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.</p>
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.</p>
Introduction.- Background
and Theory.- Proposed Method.- Application
to Human Recognition.- Experimental
Results.- Conclusions.
and Theory.- Proposed Method.- Application
to Human Recognition.- Experimental
Results.- Conclusions.
<p>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.</p>
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.</p>
Introduces a new model of a modular neural network based on a granular approach Serves as reference book for scientists and engineers interested in applying soft computing Presents recent research Includes supplementary material: sn.pub/extras
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