From local neural networks to granular neural networks: A study in information granulation

Authors:
Mingli Song;Witold Pedrycz
Affiliations:
Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada T6R 2G7;Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada T6R 2G7 and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Venue:
Neurocomputing
Year:
2011

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Abstract

In this study, we are concerned with a construction of granular neural networks (GNNs)-architectures formed as a direct result reconciliation of results produced by a collection of local neural networks constructed on a basis of individual data sets. Being cognizant of the diversity of the results produced by the collection of networks, we arrive at the concept of granular neural network, producing results in the form of information granules (rather than plain numeric entities) that become reflective of the diversity of the results generated by the contributing networks. The design of a granular neural network exploits the concept of justifiable granularity. Introduced is a performance index quantifying the quality of information granules generated by the granular neural network. This study is illustrated with the aid of machine learning data sets. The experimental results provide a detailed insight into the developed granular neural networks.