Modular network SOM

Authors:
Kazuhiro Tokunaga;Tetsuo Furukawa
Affiliations:
Department of Brain Science and Engineering, Kyushu Institute of Technology, Kitakyushu, Japan;Department of Brain Science and Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
Venue:
Neural Networks
Year:
2009

Citing 7
Cited 5

Distributed Representations, Simple Recurrent Networks, And Grammatical Structure

Machine Learning - Connectionist approaches to language learning
Self-organized formation of various invariant-feature filters in the adaptive-subspace SOM

Neural Computation
Evolutionary algorithms using a neural network like migration scheme

Integrated Computer-Aided Engineering
SOM of SOMs: self-organizing map which maps a group of self-organizing maps

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Self organization of a massive document collection

IEEE Transactions on Neural Networks
Three-dimensional neural net for learning visuomotor coordination of a robot arm

IEEE Transactions on Neural Networks
Asymptotic level density in topological feature maps

IEEE Transactions on Neural Networks

SOM of SOMs

Neural Networks
Hierarchical Architecture with Modular Network SOM and Modular Reinforcement Learning

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Requirements for the learning of multiple dynamics

WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Text clustering based on granular computing and wikipedia

RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A novel self-adaptive clustering algorithm for dynamic data

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

This study aims to develop a generalized framework of an SOM called a modular network SOM (mnSOM). The mnSOM has an array structure consisting of functional modules that are trainable neural networks, e.g., multi-layer perceptrons (MLPs), instead of the vector units of the conventional SOM. In the case of MLP-modules, an mnSOM learns a group of systems or functions in terms of the input-output relationships in parallel with generating a feature map of them. Thus an mnSOM with MLP modules is an SOM in function space rather than in vector space. In this paper, first, as an example, we focus on a class of mnSOM that consists of MLP modules and introduce the architecture and algorithm. Then, a more generalized framework is described. Finally, some simulation results of an MLP-module-mnSOM are presented.