Feature selection by a distance measure method of subnormal and non-convex fuzzy sets

2021 ◽  
pp. 1-7
Author(s):  
Letao Qu ◽  
Bohyun Wang ◽  
Joon S. Lim
Keyword(s):  
Feature Selection ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Critical Role ◽  
Classification Systems ◽  
Fuzzy Rules ◽  
Fuzzy Classification ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods.

Analysis and Research of Direction Relation Based on Fuzzy Description

2011 ◽  
Vol 393-395 ◽  
pp. 1102-1105
Author(s):  
Yong Shan Liu ◽  
Yan Li
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Degree ◽  
Membership Functions ◽  
Fuzzy Matrix ◽  
Fuzzy Membership Functions ◽  
Fuzzy Objects ◽  
Direction Relations ◽  
Fuzzy Description

A fuzzy membership function was defined for each direction to predict the membership degree that an object pertains to a certain direction. Nine fuzzy membership functions were defined to describe the direction relations between fuzzy objects and crisp objects with corresponding fuzzy sets. Direction relations were described by a 3×3 fuzzy matrix, which was computed by an aggregation operator defined on the nine fuzzy sets. The fuzzy matrices and crisp matrices of direction relations between fuzzy objects and crisp objects were computed respectively, and comparison of fuzzy matrices with crisp ones reveals that the proposed fuzzy approach is more effective than existing crisp method.

scholarly journals Computing with words with the use of inverse RDM models of membership functions

2015 ◽  
Vol 25 (3) ◽  
pp. 675-688 ◽  
Author(s):  
Andrzej Piegat ◽  
Marcin Pluciński
Keyword(s):  
Interval Arithmetic ◽  
Distance Measure ◽  
Specific Problem ◽  
Human Mind ◽  
Relative Distance ◽  
Fuzzy Membership ◽  
Computing With Words ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Flight Delay

Abstract Computing with words is a way to artificial, human-like thinking. The paper shows some new possibilities of solving difficult problems of computing with words which are offered by relative-distance-measure RDM models of fuzzy membership functions. Such models are based on RDM interval arithmetic. The way of calculation with words was shown using a specific problem of flight delay formulated by Lotfi Zadeh. The problem seems easy at first sight, but according to the authors’ knowledge it has not been solved yet. Results produced with the achieved solution were tested. The investigations also showed that computing with words sometimes offers possibilities of achieving better problem solutions than with the human mind.

scholarly journals Adaptive Fuzzy Sliding Mode Controller for Robot Manipulator: Effect on External Interferences

2019 ◽  
Vol 9 (1) ◽  
pp. 5024-5031
Keyword(s):  
Sliding Mode ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Fuzzy Rules ◽  
System Model ◽  
Fuzzy Membership ◽  
Sliding Mode Controller ◽  
Membership Functions ◽  
Adaptive Fuzzy ◽  
Fuzzy Membership Functions

In the previous decades, the SMC approach has attained unique consideration as this technique offers a systematic model to maintain robust performance and asymptotic stability. As robotic manipulators turn out to be gradually more significant in industrial automation, robotic manipulators by means of SMC have raised as a significant region of research. Hence, this paper intends to model and establish an adaptive sliding mode controller (SMC) for robotic manipulator. As it is not feasible to match up the SMC functions with the system model each time, this paper implements a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. Consequently, it derives the obtained features as fuzzy rules. In the subsequent phase, it signifies the derived fuzzy rules depending on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the external disturbances and noise and validates the effectiveness of the proposed and conventional models.

Evolving Neurofuzzy System by Hybrid Soft Computing Approaches for System Identification

2001 ◽  
Vol 5 (4) ◽  
pp. 220-228 ◽  
Author(s):  
Shigeyasu Kawaji ◽  
◽  
Yuehui Chen
Keyword(s):  
Search Algorithm ◽  
Random Search ◽  
Hybrid Approach ◽  
Fuzzy Rules ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Structural Representation ◽  
Fuzzy Membership Functions ◽  
System Calculation ◽  
Selection Of

This paper studies optimizing neurofuzzy system using a hybrid approach of a modified probabilistic incremental program evolution algorithm (MPIPE), random search algorithm, and evolutionary programming (EP). Neurofuzzy system is a combination of fuzzy system and neural network. The performance of a neurofuzzy system depends largely on selection of fuzzy membership functions, partition of input space and fuzzy rules. Two neurofuzzy models, additive and direct, are proposed in which neurofuzzy system calculation is based on tree structural representation. Without prior knowledge of the plant, parameters of fuzzy membership functions, the number of fuzzy rules and weights of neurofuzzy system are optimized using a hybrid method of MPIPE and EP algorithms simultaneously. Simulation results for identification of nonlinear systems show the feasibility and effectiveness of the proposed method.

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.

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