An Adaptive Fuzzy Network

Zheng TANG; Okihiko ISHIZUKA; Hiroki MATSUMOTO

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Zheng TANG, Okihiko ISHIZUKA, Hiroki MATSUMOTO, "An Adaptive Fuzzy Network" in IEICE TRANSACTIONS on Fundamentals, vol. E75-A, no. 12, pp. 1826-1828, December 1992, doi: .
Abstract: An adaptive fuzzy network (AFN) is described that can be used to implement most of fuzzy logic functions. We introduce a learning algorithm largely borrowed from backpropagation algorithm and train the AFN system for several typical fuzzy problems. Simulations show that an adaptive fuzzy network can be implemented with the proposed network and algorithm, which would be impractical for a conventional fuzzy system.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e75-a_12_1826/_p

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@ARTICLE{e75-a_12_1826,
author={Zheng TANG, Okihiko ISHIZUKA, Hiroki MATSUMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Adaptive Fuzzy Network},
year={1992},
volume={E75-A},
number={12},
pages={1826-1828},
abstract={An adaptive fuzzy network (AFN) is described that can be used to implement most of fuzzy logic functions. We introduce a learning algorithm largely borrowed from backpropagation algorithm and train the AFN system for several typical fuzzy problems. Simulations show that an adaptive fuzzy network can be implemented with the proposed network and algorithm, which would be impractical for a conventional fuzzy system.},
keywords={},
doi={},
ISSN={},
month={December},}

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TY - JOUR
TI - An Adaptive Fuzzy Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1826
EP - 1828
AU - Zheng TANG
AU - Okihiko ISHIZUKA
AU - Hiroki MATSUMOTO
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E75-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 1992
AB - An adaptive fuzzy network (AFN) is described that can be used to implement most of fuzzy logic functions. We introduce a learning algorithm largely borrowed from backpropagation algorithm and train the AFN system for several typical fuzzy problems. Simulations show that an adaptive fuzzy network can be implemented with the proposed network and algorithm, which would be impractical for a conventional fuzzy system.
ER -