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Type-2 fuzzy logic systems.
~
Karnik, Nilesh Naval.
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Type-2 fuzzy logic systems.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Type-2 fuzzy logic systems./
Author:
Karnik, Nilesh Naval.
Description:
373 p.
Notes:
Source: Dissertation Abstracts International, Volume: 60-02, Section: B, page: 0767.
Contained By:
Dissertation Abstracts International60-02B.
Subject:
Engineering, Electronics and Electrical. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9919062
ISBN:
0599181168
Type-2 fuzzy logic systems.
Karnik, Nilesh Naval.
Type-2 fuzzy logic systems.
- 373 p.
Source: Dissertation Abstracts International, Volume: 60-02, Section: B, page: 0767.
Thesis (Ph.D.)--University of Southern California, 1998.
This dissertation introduces a new class of fuzzy logic systems (FLS), type-2 fuzzy logic systems, which make use of type-2 fuzzy sets for representing linguistic and/or numerical uncertainty. Type-2 fuzzy sets are fuzzy sets having fuzzy membership functions, i.e., the membership grade of each element of such a set is an ordinary (type-1) fuzzy set in [0, 1]. Type-2 sets are useful in circumstances where it is difficult to define the exact membership function for a fuzzy set.
ISBN: 0599181168Subjects--Topical Terms:
626636
Engineering, Electronics and Electrical.
Type-2 fuzzy logic systems.
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Type-2 fuzzy logic systems.
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373 p.
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Source: Dissertation Abstracts International, Volume: 60-02, Section: B, page: 0767.
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Adviser: Jerry M. Mendel.
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Thesis (Ph.D.)--University of Southern California, 1998.
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This dissertation introduces a new class of fuzzy logic systems (FLS), type-2 fuzzy logic systems, which make use of type-2 fuzzy sets for representing linguistic and/or numerical uncertainty. Type-2 fuzzy sets are fuzzy sets having fuzzy membership functions, i.e., the membership grade of each element of such a set is an ordinary (type-1) fuzzy set in [0, 1]. Type-2 sets are useful in circumstances where it is difficult to define the exact membership function for a fuzzy set.
520
$a
Since the results in the existing type-2 fuzzy logic literature are not sufficient for our work, we start with the very basic operations of unions, intersections and complements of type-2 sets, and develop the results that we need in order to implement a type-2 FLS. In the process of this development, we study, in detail, set theoretic and algebraic operations for type-2 sets, properties of membership grades of type-2 sets, and type-2 relations and their compositions. We also examine, in great detail, the operations of "type-reduction" and defuzzification in a type-2 FLS. Type-reduction is a term that we have coined for "extended" versions of type-1 defuzzification methods. We provide results that considerably simplify the implementation of Gaussian, interval and triangular type-2 fuzzy logic systems. Whenever actual results are difficult to implement or generalize, we provide practical approximations.
520
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We demonstrate the use of a type-2 FLS with the help of two examples: managing rules collected by means of a survey, and time-series prediction. In the survey example, we show how the linguistic uncertainty about membership functions of the FLS, as well as rule uncertainty from multiple experts, each of whom may give different answers to the same question, can be handled in the type-2 framework. In the time-series example, we show how information about the noise in the training data can be incorporated in a type-2 FLS to obtain bounds on the output and also better predictions.
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Finally, we present our conclusions and some directions for future research.
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School code: 0208.
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Engineering, Electronics and Electrical.
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University of Southern California.
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60-02B.
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Mendel, Jerry M.,
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advisor
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1998
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9919062
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