Comparison between type-1 fuzzy membership functions for sign language applications

2016 ◽  
Author(s):  
Sriparna Saha ◽  
Saurav Bhattacharya ◽  
Amit Konar
Keyword(s):  
Sign Language ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

scholarly journals Automatic Methods for Generation of Type-1 and Interval Type-2 Fuzzy Membership Functions

2015 ◽  
Vol 11 (9) ◽  
pp. 976-987 ◽  
Author(s):  
Andréia Alves dos Santos Schwaab ◽  
Silvia Modesto Nassar ◽  
Paulo José de Freitas Filho
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Automatic Methods ◽  
Interval Type

Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions

2018 ◽  
Vol 26 (2) ◽  
pp. 681-693 ◽  
Author(s):  
Desh Raj ◽  
Aditya Gupta ◽  
Bhuvnesh Garg ◽  
Kenil Tanna ◽  
Frank Chung-Hoon Rhee
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

scholarly journals A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1932
Author(s):  
Muhammad Hamza Azam ◽  
Mohd Hilmi Hasan ◽  
Saima Hassan ◽  
Said Jadid Abdulkadir
Keyword(s):  
Membership Function ◽  
Clustering Algorithm ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
Function Type ◽  
Fuzzy C Means ◽  
Fuzzy Membership Functions ◽  
Fuzzy C Means Algorithm

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

A method for automatic generation of fuzzy membership functions for mobile device’s characteristics based on Google Trends

2013 ◽  
Vol 29 (2) ◽  
pp. 510-517 ◽  
Author(s):  
Aitor Almeida ◽  
Pablo Orduña ◽  
Eduardo Castillejo ◽  
Diego López-de-Ipiña ◽  
Marcos Sacristán
Keyword(s):  
Automatic Generation ◽  
Google Trends ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Fuzzy membership functions of procurement selection criteria

2002 ◽  
Vol 20 (3) ◽  
pp. 285-296 ◽  
Author(s):  
S. Thomas Ng ◽  
Duc Thanh Luu ◽  
Swee Eng Chen ◽  
Ka Chi Lam
Keyword(s):  
Selection Criteria ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions
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