Self Evolving Modular Network

Kazuhiro TOKUNAGA  Nobuyuki KAWABATA  Tetsuo FURUKAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.5   pp.1506-1518
Publication Date: 2012/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1506
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Biocybernetics, Neurocomputing
self-organizing maps,  modular networks,  evolving SOM,  modular network SOM,  online learning,  

Full Text: PDF(2.2MB)>>
Buy this Article

We propose a novel modular network called the Self-Evolving Modular Network (SEEM). The SEEM has a modular network architecture with a graph structure and these following advantages: (1) new modules are added incrementally to allow the network to adapt in a self-organizing manner, and (2) graph's paths are formed based on the relationships between the models represented by modules. The SEEM is expected to be applicable to evolving functions of an autonomous robot in a self-organizing manner through interaction with the robot's environment and categorizing large-scale information. This paper presents the architecture and an algorithm for the SEEM. Moreover, performance characteristic and effectiveness of the network are shown by simulations using cubic functions and a set of 3D-objects.