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Majalah Ilmiah UNIKOM

Vol.8, No. 2

215

H a l a m a n

Muhammad Aria

A

Comparative Study of Fuzzy Logic Con-

trollers for Autonomous Robots

An

Introduction to Type-2 TSK Fuzzy Logic

Systems

tems Conference Proceedings, pp III-

1534 – III-1539.

Jerry M. Mendel and Hongwei Wu (2007),

New Results About the Centroid of An

Interval Type-2 Fuzzy Set, Including the

Centroid of a Fuzzy Granule

Sciences an International Journal, pp

360 – 377.

An Interval

Type-II Robust Fuzzy Logic Controller for

a Static Compensator in a Multimachine

Power System

ference on Neural Networks, pp 2242 –

2244.

Type – 2 FLCs : A New

Generation of Fuzzy Controllers

Computational Intelligence Magazine,

vol 2, no.1, pp. 30 – 43

Jang, J.-S.R., C.-T. Sun, dan E Mizzutami,

Neuro-Fuzzy and Soft Computing,

tice Hall Inc., 1997

Inter-

val Type-1 Non-Singleton Type-2 TSK

Fuzzy Logic Systems Using the Hybrid

Training Method RLS-BP

sium on Foundations of Computational

Intelligence

Jerry M. Mendel, Robert I. John and Feilong

Interval Type-2 Fuzzy Logic

Systems Made Simple

tions on Fuzzy Systems, vol 14, no 6, pp

808 – 821.

Type-

2 Fuzzistics for Symmetric Interval Type-

2 Fuzzy Sets: Part 1, Forward Problems

IEEE Transactions on Fuzzy Systems, vol

14, no 6, pp 781 – 792.

Qun Ren, Luc Baron and Marek Balazinski

Type-2 Takagi-Sugeno-Kang

Fuzzy Logic Modeling using Subtractive

Clustering

Intro-

duction to Type-2 Fuzzy Logic Systems

University of Southern California, Los

Angeles

Type-2 Fuzzy Logic Ap-

proach for Short Term Traffic Forecast-

ing

153