RKT2FCM: RBF Kernel-Based Type-2 Fuzzy Clustering

2020 ◽  
Author(s):  
Sonika Dahiya ◽  
Anushika Gosain ◽  
Saijal Gupta
Keyword(s):  
Fuzzy Clustering ◽  
Rbf Kernel

GT2-CFC: General Type-2 Collaborative Fuzzy Clustering method

2021 ◽  
Author(s):  
Fariba Salehi ◽  
Mohammad Reza Keyvanpour ◽  
Arash Sharifi
Keyword(s):  
Fuzzy Clustering ◽  
General Type ◽  
Clustering Method ◽  
Fuzzy Clustering Method

Parameter Prediction Based on Type-2 Fuzzy Clustering

2018 ◽  
Vol 26 (06) ◽  
pp. 877-892 ◽  
Author(s):  
Türkan Erbay Dalkiliç ◽  
Seda Sağirkaya
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Clustering ◽  
Alternative Methods ◽  
Data Set ◽  
Clustering Problem ◽  
Fuzzy Clustering Method ◽  
Parameter Prediction

In regression analysis, the data have different distributions which requires to go beyond the classical analysis during the prediction process. In such cases, the analysis method based on fuzzy logic is preferred as alternative methods. There are couple important steps in the regression analysis based on fuzzy logic. One of them is identification of the clusters that generate the data set, the other is the degree of memberships that are determined the grades of the contributions of the data contained in these clusters. In this study, parameter prediction based on type-2 fuzzy clustering is discussed. Firstly, type-1 fuzzy clustering problem was solved by the fuzzy c-means (FCM) method when the fuzzifier index is equal to two. Then the fuzzifier index m is defined as interval number. The membership degrees to the sets are determined by type-2 fuzzy clustering method. Membership degree obtained as a result of clustering based on type-1 and type-2 fuzzy logic are used as weight and parameter prediction using these membership degrees that determined by the proposed algorithm. Finally, the prediction result of the type-1 and type-2 fuzzy clustering parameter is compared with the error criterion based on the difference between observed values and the predicted values.

A New Optimization Approach to Clustering Fuzzy Data for Type-2 Fuzzy System Modeling

2012 ◽  
pp. 499-508
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy System ◽  
Distance Measure ◽  
System Model ◽  
Rule Base ◽  
Optimization Approach ◽  
Fuzzy Data ◽  
System Models ◽  
Fuzzy System Models

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valuate Type-2 fuzzy clustering approach. In this index, all operations are fuzzy and the minimization method is fuzzy ranking with Hamming distance. The proposed Type-2 fuzzy clustering method is used for development of indirect approach to Type-2 fuzzy modeling, where the rules are extracted from clustering fuzzy numbers (Zadeh, 1965). Then, the Type-2 fuzzy system is tuned by an inference algorithm for optimization of the main parameters of Type-2 parametric system. In this case, the parameters are: Schweizer and Sklar t-Norm and s-Norm, a-cut of rule-bases, combination of FATI and FITA inference approaches, and Yager parametric defuzzification. Finally, the proposed Type-2 fuzzy system model is applied in prediction of the steel additives in steelmaking process. It is shown that, the proposed Type-2 fuzzy system model is superior in comparison with multiple regressions and Type-1 fuzzy system model, in terms of the minimization the effect of uncertainty in the rule-base fuzzy system models an error reduction.

COMPARISON OF TYPE-2 FUZZY CLUSTERING-BASED CASCADE CLASSIFIER MODELS FOR ECG ARRHYTHMIAS

2014 ◽  
Vol 26 (06) ◽  
pp. 1450075
Author(s):  
Rahime Ceylan ◽  
Yüksel Özbay ◽  
Bekir Karlik
Keyword(s):  
Feature Extraction ◽  
Fuzzy Clustering ◽  
Classification Algorithms ◽  
Ecg Signal ◽  
Classification Models ◽  
Cascade Classifier ◽  
Training Set ◽  
Feature Sets

The aim of this study is to present a comparison of the novel cascade classifier models based on fuzzy clustering and feature extraction techniques according to efficiency. These models are composed of three subsystems: The first subsystem is constituted by fuzzy clustering technique to choose the best patterns that ideally show its class attributes in dataset. The second subsystem consists of discrete wavelet transform (DWT) which realizes feature extraction procedure on selected patterns by using fuzzy c-means clustering. The last subsystem implements the classification of extracted features for each pattern using classification algorithm. In this paper, type-2 fuzzy c-means (T2FCM) clustering is used in the first subsystem of the proposed classification models and the new training set is obtained. In the second subsystem, the features of the obtained new training set are extracted with DWT; hence, three different feature sets along with different number of features are formed using Daubechies-2 wavelet function. In the last subsystem of the model, the feature sets are classified using classification algorithm. Here, two different classification algorithms, neural network (NN) and support vector machine (SVM), are used for comparison. Thus, two classification models are implemented and named as T2FCWNN (classifier is NN) and T2FCWSVM (classifier is SVM), respectively. This proposed classifier models have been applied to classify electrocardiogram (ECG) signals. One of the goals of this study is to present a fast and efficient classifier. For this reason, high accuracy rate is been aimed for classification of RR intervals in ECG signal. So, we have utilized T2FCM and WTs to improve the performance of the classification algorithms. Both training and testing set for classifier models have included 12 ECG signal classes. Well-known back propagation algorithm has been used for training of neural networks (NNs). The best testing results have been obtained with 99% recognition accuracy with T2FCWNN-2.

Type-2 Fuzzy Clustering

2014 ◽  
pp. 153-166
Author(s):  
Rafik Aziz Aliev ◽  
Babek Ghalib Guirimov
Keyword(s):  
Fuzzy Clustering

EUDFC - Enhanced Unequal Distributed Type-2 Fuzzy Clustering Algorithm

2019 ◽  
Vol 19 (12) ◽  
pp. 4705-4716 ◽  
Author(s):  
Antonio Jesus Yuste-Delgado ◽  
Juan Carlos Cuevas-Martinez ◽  
Alicia Trivino-Cabrera
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm

Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means

2007 ◽  
Vol 15 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Cheul Hwang ◽  
Frank Chung-Hoon Rhee
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Approach ◽  
Interval Type

Multi-central general type-2 fuzzy clustering approach for pattern recognitions

2016 ◽  
Vol 328 ◽  
pp. 172-188 ◽  
Author(s):  
S. Malek Mohamadi Golsefid ◽  
M.H. Fazel Zarandi ◽  
I.B. Turksen
Keyword(s):  
Fuzzy Clustering ◽  
General Type ◽  
Clustering Approach
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