Shadowed Type-2 Fuzzy Logic Systems

Wijayasekara, Dumidu; Linda, Ondrej; Manic, Milos

doi:10.1109/t2fzz.2013.6613294

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…Higher-order fuzzy clustering algorithms are very well suited to deal with the high levels of uncertainties present in the majority of real-world applications. However, the immense computational complexity associated with such clustering algorithms has been a great obstacle for the practical applications [16]. Now, we focus our attention on a well-known index from the second category which is the partition coefficient and exponential separation index (PCAES) proposed by Wu and Yang [21].…”

Section: Other Approachesmentioning

confidence: 99%

A New Validity Index for Fuzzy-Possibilistic C-Means Clustering

2021

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In some complicated datasets, due to the presence of noisy data points and outliers, cluster validity indices can give conflicting results in determining the optimal number of clusters. This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic)FP ( index, which works well in the presence of clusters that vary in shape and density. Moreover, FPCM like most of the clustering algorithms is susceptible to some initial parameters. In this regard, in addition to the number of clusters, FPCM requires a priori selection of the degree of fuzziness ( m ) and the degree of typicality ( ). Therefore, we presented an efficient procedure for determining an optimal value for m and . The proposed approach has been evaluated using several synthetic and real-world datasets. Final computational results demonstrate the capabilities and reliability of the proposed approach compared with several well-known fuzzy validity indices in the literature. Furthermore, to clarify the ability of the proposed method in real applications, the proposed method is implemented in microarray gene expression data clustering and medical image segmentation.

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Section: Other Approachesmentioning

confidence: 99%

A New Validity Index for Fuzzy-Possibilistic C-Means Clustering

2021

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“…The purpose of the Shadowed Type-2 FIS [19] consists of reducing the computational cost represented by the use of α-planes, and the main characteristic of this proposal is to model the GT2FIS with only two optimal α-planes, eliminating the excessive precision, all that aforementioned knowledge is based on the concepts proposed by Pedrycz, who tells us about the theory of shadowed sets in [20] [21] [22]. Equation 4 expresses the explanation of the shadowed set concept, which consists on performing two -cuts on a fuzzy set, with and values, which are based on these -cuts.…”

Section: Type-2 Fuzzy Systems and Shadowed Setsmentioning

confidence: 99%

Differential Evolution With Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers

Ochoa¹,

Castillo²,

Melín³

et al. 2021

Preprint

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This work is mainly focused on improving the differential evolution algorithm with the utilization of shadowed and general type 2 fuzzy systems to dynamically adapt one of the parameters of the evolutionary method. In this case, the mutation parameter is dynamically moved during the evolution process by using a shadowed and general type-2 fuzzy systems. The main idea of this work is to make a performance comparison between using shadowed and general type 2 fuzzy systems as controllers of the mutation parameter in differential evolution. The performance is compared with the problem of optimizing fuzzy controllers for a D.C. Motor. Simulation results show that general type-2 fuzzy systems are better when higher levels of noise are considered in the controller.

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“…In order to reduce the computational cost of the α-planes representation, the Shadowed Type-2 FIS was introduced in [23]. The proposal focused on modeling the GT2 FIS with only two optimal α-planes, eliminating the excessive precision, and the selection of the optimal α-planes was performed through the concepts that Pedrycz proposed for the shadowed set theory [14,[24][25][26].…”

Section: Type-2 Fuzzy Systems and Shadowed Setsmentioning

confidence: 99%

“…In [23], Linda and Manic proposed the use of shadowed sets in the secondary axis of the GT2 FIS, finding, in this way, the optimal α and β values and then using these values as α-planes. In this way, the computational cost is reduced, and the implementation of GT2 FIS for dynamic parameter adaptation in metaheuristic algorithms is allowed.…”

Section: Trapezoidal St2 Mfmentioning

confidence: 99%

Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

et al. 2019

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Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.

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Shadowed Type-2 Fuzzy Logic Systems

Cited by 16 publications

References 24 publications

A New Validity Index for Fuzzy-Possibilistic C-Means Clustering

A New Validity Index for Fuzzy-Possibilistic C-Means Clustering

Differential Evolution With Shadowed and General Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Optimal Design of Fuzzy Controllers

Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

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