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2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95)
Fuzzy reasoning method for smooth interpolation
Dunedin, New Zealand
November 20-November 23
ISBN: 0-8186-7174-2
ASCII Text
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S. Nakamura, E. Uchino, T. Yamakawa, "Fuzzy reasoning method for smooth interpolation," Artificial Neural Networks and Expert Systems, New Zealand Conference, pp. 120, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995.
 
 
BibTex
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@article{ 10.1109/ANNES.1995.499454,
author = {S. Nakamura and E. Uchino and T. Yamakawa},
title = {Fuzzy reasoning method for smooth interpolation},
journal ={Artificial Neural Networks and Expert Systems, New Zealand Conference},
volume = {0},
year = {1995},
isbn = {0-8186-7174-2},
pages = {120},
doi = {http://doi.ieeecomputersociety.org/10.1109/ANNES.1995.499454},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Artificial Neural Networks and Expert Systems, New Zealand Conference
TI - Fuzzy reasoning method for smooth interpolation
SN - 0-8186-7174-2
SP
EP
A1 - S. Nakamura,
A1 - E. Uchino,
A1 - T. Yamakawa,
PY - 1995
KW - interpolation; inference mechanisms; fuzzy set theory; digital simulation; simulation; smooth interpolation; fuzzy reasoning method; humans; fluctuation; neighboring data; fuzzy rule; data pairs; computer simulations; practical data
VL - 0
JA - Artificial Neural Networks and Expert Systems, New Zealand Conference
ER -
 
S. Nakamura, Kyushu Inst. of Technol., Fukuoka, Japan
E. Uchino, Kyushu Inst. of Technol., Fukuoka, Japan
T. Yamakawa, Kyushu Inst. of Technol., Fukuoka, Japan
The paper describes a fuzzy reasoning method whose result belongs to the C/sup 1/ class. The proposed fuzzy reasoning method refers to interpolation by man. We suppose that humans consider two directions while interpolating. A direction of fluctuation at a given point is one, and a direction toward neighboring data is the other. Both directions are reflected in the consequent part of the fuzzy rule. It is not necessary to give them as knowledge, these are only decided as given data pairs. Even if new data is given, if is not necessary to increase a rule. The reasoning result can correspond to new data without increasing a rule. The effectiveness of the present method as verified by applications to practical data and by computer simulations.
Index Terms:
interpolation; inference mechanisms; fuzzy set theory; digital simulation; simulation; smooth interpolation; fuzzy reasoning method; humans; fluctuation; neighboring data; fuzzy rule; data pairs; computer simulations; practical data
Citation:
S. Nakamura, E. Uchino, T. Yamakawa, "Fuzzy reasoning method for smooth interpolation," annes, pp.120, 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems (ANNES '95), 1995
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