Self-organized function localization neural network

Takafumi Sasakawa, Jinglu Hu, Kotaro Hirasawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper presents a self-organizing function localization neural network (FLNN) inspired by Hebb's cell assembly theory about how the brain worked. The proposed self-organizing FLNN consists of two parts: main part and control part. The main part is an ordinary 3-layered feedforward neural network, but each hidden neuron contains a signal from the control part, controlling its firing strength. The control part consists of a SOM network whose outputs are associated with the hidden neurons of the main part. Trained with an unsupervised learning, SOM control part extracts structural features of input-output spaces and controls the firing strength of hidden neurons in the main part. Such self-organizing FLNN realizes capabilities of function localization and learning. Numerical simulations show that the self-organizing FLNN has superior performance than an ordinary neural network.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1463-1468
Number of pages6
DOIs
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period04/7/2504/7/29

ASJC Scopus subject areas

  • Software

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  • Cite this

    Sasakawa, T., Hu, J., & Hirasawa, K. (2004). Self-organized function localization neural network. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (pp. 1463-1468). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2). https://doi.org/10.1109/IJCNN.2004.1380168