Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation, 1st ed. 2016 SpringerBriefs in Computational Intelligence Series

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.

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