NDLI: Modified counterpropagation employing neo fuzzy neurons and its application to system modeling

Content Provider	IEEE Xplore Digital Library
Author	Horio, K. Yamakawa, T.
Copyright Year	2001
Description	Author affiliation: Dept. of Brain Sci. & Eng., Kyushu Inst. of Technol., Iizuka, Japan (Horio, K.)
Abstract	In this paper, a modified counterpropagation employing neo fuzzy neurons is proposed. The counterpropagation is a network which can obtain a mapping from inputs to outputs by competitive learning and supervised learning. In the conventional counterpropagation, network outputs axe obtained by sum of outputs of units in previous layer, thus it is not effective to apply the counterpropagation to the system including heavy nonlinearity. In order to develop modeling ability, we employ neo fuzzy neurons, which are neuron models with nonlinear synapses, instead of sum for obtaining network outputs. The effectiveness and the validity of the proposed modified counterpropagation are verified by applying it to system modeling.
Sponsorship	China Assoc. Sci. & Technol.(CAST)
Starting Page	50
Ending Page	55
File Size	442826
Page Count	6
File Format	PDF
ISBN	0780370104
DOI	10.1109/ICII.2001.983718
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Neurons Supervised learning Neural networks Systems engineering and theory Fuzzy neural networks System identification Modeling Biological neural networks Fuzzy systems
Content Type	Text
Resource Type	Article

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