Type 2 fuzzy set analysis in management surveys

Auephanwiriyakul, Sansanee; Adrian, Allyson D.; Keller, J.M.

doi:10.1109/fuzz.2002.1006695

Cited by 21 publications

(11 citation statements)

References 11 publications

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“…Presently, type-2 fuzzy logic system is used with significant success in the field of decision making [2,3,4,5,6], mobile robotics [7,8], control [9,10], prior processing of data [11], forecasting accuracy [12], noise reduction [13], survey processing [14,15], prediction interval construction [16,17], clustering [18], intelligent environment realization [19] and time series forecasting [20,14,21,22,23,24,25,26], to name a few. This vast field of application generally utilizes high research concentration on computationally simpler version of general type-2 fuzzy sets, interval type-2 fuzzy set (IT2FS) and corresponding interval type-2 fuzzy logic system (IT2FLS) [27,28,29].…”

Section: Introductionmentioning

confidence: 99%

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Salaken

Khosravi

Nahavandi

et al. 2015

International Journal of Approximate Reasoning

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Please cite this article in press as: S.M. Salaken et al., Approximation of centroid end-points and switch points for replacing type reduction algorithms, International Journal of Approximate Reasoning (2015), http://dx. AbstractDespite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

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Section: Introductionmentioning

confidence: 99%

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Salaken

Khosravi

Nahavandi

et al. 2015

International Journal of Approximate Reasoning

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show abstract

“…(2) The principle of maximum uncertainty, which states that a conclusion should maximize the relevant uncertainty within constraints given by the verified premises, is widely used within classical probability framework [14,15,56]. (3) The principle of uncertainty invariance, which states that the amount of uncertainty should be preserved in each transformation of uncertainty from one mathematical framework to another, is widely studied in the context of probability-possibility transformations [21,32,35,65].…”

Section: Introductionmentioning

confidence: 99%

“…Since the boundaries of T2 FSs are blurred, they are especially useful in circumstances where it is difficult to determine an exact membership grade [46]. To date, interval T2 (IT2) FSs are the most widely used T2 FSs, and have been used successfully for decision making [76,55,59,66], time-series forecasting [46,4], survey processing [46,3,42], document retrieval [8], speech recognition [88,45], noise cancellation [12,54], word modeling [50,72,42], clustering [57], control [71,70,38,24,13,43,20,41,58,11,44,1], wireless communication [40,60], webshopping [23], linguistic summarization of database [53,52], etc.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty measures for interval type-2 fuzzy sets

Wu¹,

Mendel²

2007

Information Sciences

339

128

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“…Other techniques [17] have been presented to perform color clustering in a color space [20], based on T1 fuzzy sets and fuzzy homogeneity (or fuzzy entropy). The major concern of these techniques is spatial ambiguity among pixels, which has inherent vagueness rather than randomness.…”

Section: Scope Of Workmentioning

confidence: 99%

Color Image Segmentation using Type-2 Fuzzy Sets

Maity¹,

Sil²

2009

IJCEE

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Abstract--Domain knowledge of real life problems are often uncertain, imprecise and inexact, therefore create difficulty in decision making while solving by conventional approaches.Among various methods of handling uncertainties, fuzzy logic has been most intensively studied almost over four decades. Fuzzy logic (FL) explores human reasoning power using linguistic terms, which are modeled as type-1 fuzzy sets and represented by membership functions (MF). However, the MF of type-1 fuzzy set is crisp and cannot tackle all kind of uncertainties. Introducing type-2 fuzzy sets, an extension of type-1 simplifies the problem where the MF is itself fuzzy with three dimension representations. Moreover, a type-2 fuzzy set maps elements of a crisp domain to type-1 fuzzy numbers bounded in the range [0, 1]. Research on type-2 fuzzy sets is still in its infancy and very recently has been applied on emerging areas that need development of more efficient and robust systems. The aim of the review paper is to describe type-2 fuzzy systems for managing uncertainties, presenting the frontier research areas where type-2 fuzzy logic has been applied and proposes algorithm on application of type-2 fuzzy sets in color image segmentation.

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Type 2 fuzzy set analysis in management surveys

Cited by 21 publications

References 11 publications

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Uncertainty measures for interval type-2 fuzzy sets

Color Image Segmentation using Type-2 Fuzzy Sets

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