• Title/Summary/Keyword: Interval Type 2 Fuzzy Sets

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Determining the Fuzzifier Values for Interval Type-2 Possibilistic Fuzzy C-means Clustering (Interval Type-2 Possibilistic Fuzzy C-means 클러스터링을 위한 퍼지화 상수 결정 방법)

  • Joo, Won-Hee;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.27 no.2
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    • pp.99-105
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    • 2017
  • Type-2 fuzzy sets are preferred over type-1 sets as they are capable of addressing uncertainty more efficiently. The fuzzifier values play pivotal role in managing these uncertainties; still selecting appropriate value of fuzzifiers has been a tedious task. Generally, based on observation particular value of fuzzifier is chosen from a given range of values. In this paper we have tried to adaptively compute suitable fuzzifier values of interval type-2 possibilistic fuzzy c-means (IT2 PFCM) for a given data. Information is extracted from individual data points using histogram approach and this information is further processed to give us the two fuzzifier values $m_1$, $m_2$. These obtained values are bounded within some upper and lower bounds based on interval type-2 fuzzy sets.

An Interval Type-2 Fuzzy PCM Algorithm for Pattern Recognition (패턴인식을 위한 Interval Type-2 퍼지 PCM 알고리즘)

  • Min, Ji-Hee;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.1
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    • pp.102-107
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    • 2009
  • The Possibilistic C-means(PCM) was proposed to overcome some of the drawbacks associated with the Fuzzy C-means(FCM) such as improved performance for noise data. However, PCM possesses some drawbacks such as sensitivity in initial parameter values and to patterns that have relatively short distances between the prototypes. To overcome these drawbacks, we propose an interval type 2 fuzzy approach to PCM by considering uncertainty in the fuzzy parameter m in the PCM algorithm.

Prediction System Design based on An Interval Type-2 Fuzzy Logic System using HCBKA (HCBKA를 이용한 Interval Type-2 퍼지 논리시스템 기반 예측 시스템 설계)

  • Bang, Young-Keun;Lee, Chul-Heui
    • Journal of Industrial Technology
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    • v.30 no.A
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    • pp.111-117
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    • 2010
  • To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

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A Novel Approach towards use of Adaptive Multiple Kernels in Interval Type-2 Possibilistic Fuzzy C-Means (적응적 Multiple Kernels을 이용한 Interval Type-2 Possibilistic Fuzzy C-Means 방법)

  • Joo, Won-Hee;Rhee, Frank Chung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.529-535
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    • 2014
  • In this paper, we propose a hybrid approach towards multiple kernels interval type-2 possibilistic fuzzy C-means(PFCM) based on interval type-2 possibilistic fuzzy c-means(IT2PFCM) and possibilistic fuzzy c-means using multiple kernels( PFCM-MK). In case of noisy data or overlapping cluster prototypes, fuzzy C-means gives poor performance in comparison to possibilistic fuzzy C-means(PFCM). Moreover, to address the uncertainty associated with fuzzifier parameter m, interval type-2 possibilistic fuzzy C-means(PFCM) is used. Most of the practical data available are complex and non-linearly separable. In such cases using Gaussian kernels proves helpful. Therefore, in order to overcome all these issues, we have integrated multiple kernels possibilistic fuzzy C-means(PFCM) into interval type-2 possibilistic fuzzy C-means(IT2PFCM) and propose the idea of multiple kernels based interval type-2 possibilistic fuzzy C-means(IT2PFCM-MK).

(inf,sup)-HESITANT FUZZY BI-IDEALS OF SEMIGROUPS

  • PONGPUN JULATHA;AIYARED IAMPAN
    • Journal of applied mathematics & informatics
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    • v.41 no.2
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    • pp.413-437
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    • 2023
  • In this paper, we introduce the concepts of (inf, sup)-hesitant fuzzy subsemigroups and (inf, sup)-hesitant fuzzy (generalized) bi-ideals of semigroups, and investigate their properties. The concepts are established in terms of sets, fuzzy sets, negative fuzzy sets, interval-valued fuzzy sets, Pythagorean fuzzy sets, hesitant fuzzy sets, and bipolar fuzzy sets. Moreover, some characterizations of bi-ideals, fuzzy bi-ideals, anti-fuzzy bi-ideals, negative fuzzy bi-ideals, Pythagorean fuzzy bi-ideals, and bipolar fuzzy bi-ideals of semigroups are given in terms of the (inf, sup)-type of hesitant fuzzy sets. Also, we characterize a semigroup which is completely regular, a group and a semilattice of groups by (inf, sup)-hesitant fuzzy bi-ideals.

Optimized Multi-Output Fuzzy Neural Networks Based on Interval Type-2 Fuzzy Set for Pattern Recognition (패턴 인식을 위한 Interval Type-2 퍼지 집합 기반의 최적 다중출력 퍼지 뉴럴 네트워크)

  • Park, Keon-Jun;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.62 no.5
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    • pp.705-711
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    • 2013
  • In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

Comparing type-1, interval and general type-2 fuzzy approach for dealing with uncertainties in active control

  • Farzaneh Shahabian Moghaddam;Hashem Shariatmadar
    • Smart Structures and Systems
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    • v.31 no.2
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    • pp.199-212
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    • 2023
  • Nowadays fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages. Generalized type-2 fuzzy sets allow for a third dimension to capture higher order uncertainty and therefore offer a very powerful model for uncertainty handling in real world applications. With the recent advances that allowed the performance of general type-2 fuzzy logic controllers to increase, it is now expected to see the widespread of type-2 fuzzy logic controllers to many challenging applications in particular in problems of structural control, that is the case study in this paper. It should be highlighted that this is the first application of general type-2 fuzzy approach in civil structures. In the following, general type-2 fuzzy logic controller (GT2FLC) will be used for active control of a 9-story nonlinear benchmark building. The design of type-1 and interval type-2 fuzzy logic controllers is also considered for the purpose of comparison with the GT2FLC. The performance of the controller is validated through the computer simulation on MATLAB. It is demonstrated that extra design degrees of freedom achieved by GT2FLC, allow a greater potential to better model and handle the uncertainties involved in the nature of earthquakes and control systems. GT2FLC outperforms successfully a control system that uses T1 and IT2 FLCs.

Design of Type-2 FCM-based Fuzzy Inference Systems and Its Optimization (Type-2 FCM 기반 퍼지 추론 시스템의 설계 및 최적화)

  • Park, Keon-Jun;Kim, Yong-Kab;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.11
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    • pp.2157-2164
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    • 2011
  • In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.