An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

Rubio, Elid; Castillo, Oscar; Valdez, Fevrier; Melín, Patricia; González, Claudia I.; Martínez, Gonzalo

doi:10.1155/2017/7094046

Cited by 90 publications

(22 citation statements)

References 27 publications

(39 reference statements)

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“…In the work by Melin et al [11] on Edgedetection method for image processing based on generalized type-2 fuzzy logic, an approach using type-2 fuzzy for edge detection that outperforms other methods is presented. In the work of Rubio et al [19] an Extension of the Fuzzy Possibilistic Clustering Algorithm using Type-2 Fuzzy Logic Techniques is presented. In the work of Olivas et al [14] a Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers is outlined.…”

Section: Related Workmentioning

confidence: 99%

Optimization of intuitionistic and type-2 fuzzy systems in control

Castillo¹

2018

NIFS

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In this paper a method for finding the optimal design of intuitionistic fuzzy systems in control applications is presented. Traditional models work with type-0, which means using precise numbers in the models, but since the seminal work of Prof. Zadeh in 1965, type-1 fuzzy models emerged as a powerful way to represent human knowledge and natural phenomena. Later type-2 fuzzy models were also proposed by Prof. Zadeh in 1975 and more recently have been studied and applied in real world problems by many researchers. In addition, as another extension of type-1 fuzzy logic, Prof. Atanassov proposed Intuitionistic Fuzzy Logic, which is a very powerful theory in its own right. Previous works of the author and other researchers have shown that certain problems can be appropriately solved by using type-1, and others by interval type-2, while others by using intuitionistic fuzzy logic. Bio-inspired and meta-heuristic optimization algorithms have been commonly used to find the optimal design of type-1, type-2 or intuitionistic fuzzy models for applications in control, robotics, pattern recognition, time series prediction, just to mention a few. However, the question still remains about if even more complex problems (meaning non-linearity, noisy, dynamic environments, etc.) may require even higher types, orders or extensions of type-1 fuzzy models to obtain better solutions to real world problems. In this paper a framework for solving this problem of finding the optimal fuzzy model for a particular problem is presented. To the knowledge of the author, this is the first work to propose a systematic approach to solve this problem, and we envision that in the future this approach will serve as a basis for more efficient algorithms for the same task of finding the optimal fuzzy system.

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Section: Related Workmentioning

confidence: 99%

Optimization of intuitionistic and type-2 fuzzy systems in control

Castillo¹

2018

NIFS

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“…The existing research has been conducted to measure the optimum range according to the upper and lower bounds of the fuzzifier value through several repeated experiments [6]. Although these studies are ongoing, the same fuzzy constant range cannot be applied to every data [7]. As the needs on developing new method to adaptively determining the fuzzifier value for different kinds of data are growing, this paper proposes a method using a histogram based on the Interval type-2 possibilistic Fuzzy C-means (IT2 PFCM) clustering method.…”

Section: Introductionmentioning

confidence: 99%

Data Clustering Method Using Efficient Fuzzifier Values Derivation

Cho

Joo

2020

IEEE Access

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The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the number and size of clusters. In this case, the fuzzifier value m to be applied is the most important factor in improving the accuracy of data. Therefore, in this paper, we intend to perform clustering to automatically acquire the determination of m1 and m2 values that depended on existing repeated experiments. To this end, in order to increase efficiency on deriving appropriate fuzzifier value, we used the Interval type-2 possibilistic fuzzy Cmeans (IT2PFCM), clustering method to classify a given pattern. In Efficient IT2PFCM method, used for clustering, we propose an algorithm that derives suitable fuzzifier values for each data. These values also extract information from each data point through the histogram approach and Gaussian Curve Fitting method. Using the extracted information, two adaptive fuzzifier value m1 and m2 are determined. Obtained values apply the new lowest and highest membership values. In addition, it is possible to form an appropriate fuzzy area on each cluster by only taking advantage of the characteristics of IT2PFCM, which reduces uncertainty. This doesn't only improve the accuracy of clustering of measured sensor data, but can also be used without additional procedures such as data labeling or the provision of prior information. It is also efficient at monitoring numerous sensors, managing and verifying sensor data collected in real time such as smart cities. Eventually, in this study, the proposed method is to improve IT2PFCM performance on accurate and quick clustering of large amount of complex data such as Internet of Things (IoT).

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“…In this algorithm, Type-2 Fuzzy Logic Techniques were used to increase the e¢ ciency of the Fuzzy Probabilistic C-Means (FPCM) method. In addition, the performance of the method was controlled by experimental data [14].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation by type-2 fuzzy logic in case that data set has outlier

Dalkılıç¹,

Kula²,

Tolan³

2020

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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One of the problems encountered in estimating the unknown parameters of the regression models is the presence of outliers in the data set. This situation may cause problems in providing some assumptions such as the normal distribution for the parameter estimation process and the homogeneity of the variances. The case of the presence of outlier observations in the data set, estimation methods based on fuzzy logic that can be minimized the level of impact of this data are emerged as available methods. If fuzzy logic is used in regression analysis, there are two main steps for parameter estimation. The …rst of these is to de…ne the clusters that compose the data set, and the other is calculate the degree of membership to determining the contributions of the data to each model for the clusters. In this study, type-2 fuzzy clustering algorithm de…ned as an expansion of fuzzy c-means algorithm in the determination of membership degrees of data sets was bene…ted. The presence of outliers in the data set is addressed. An algorithm has been proposed to estimate the unknown belonging to parameters of the regression model using the membership degrees obtained relating to the cluster elements. The parameters were estimated using regression methods to examine the e¤ectiveness of the algorithm that called robust methods, and the results obtained were compared.

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An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

Cited by 90 publications

References 27 publications

Optimization of intuitionistic and type-2 fuzzy systems in control

Optimization of intuitionistic and type-2 fuzzy systems in control

Data Clustering Method Using Efficient Fuzzifier Values Derivation

Parameter estimation by type-2 fuzzy logic in case that data set has outlier

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