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231223s1996 xx ||||| 00| ||eng c |
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|a pubmed25n0592.xml
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|a (NLM)18263063
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Lee, K M
|e verfasserin
|4 aut
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| 245 |
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|a Fuzzy inference neural network for fuzzy model tuning
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|c 1996
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 02.10.2012
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| 500 |
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|a Date Revised 11.02.2008
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| 500 |
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|a published: Print
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| 500 |
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For this purpose, this paper proposes a fuzzy neural network model which can embody fuzzy models. The proposed model provides the functions to perform fuzzy inference and to tune the parameters for the shape of antecedent linguistic terms, the relative importance degrees of rules, and the relative importance degrees of antecedent linguistic terms in rules. In addition, to show its applicability, we perform some experiments and present the results
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Kwak, D H
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hyung, L K
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
|d 1996
|g 26(1996), 4 vom: 15., Seite 637-45
|w (DE-627)NLM098252887
|x 1941-0492
|7 nnns
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| 773 |
1 |
8 |
|g volume:26
|g year:1996
|g number:4
|g day:15
|g pages:637-45
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| 912 |
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|a AR
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|d 26
|j 1996
|e 4
|b 15
|h 637-45
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