On the Graphical Representation of Intuitionistic Membership Functions for Its Use in Intuitionistic Fuzzy Inference Systems

2016 ◽  
pp. 115-126 ◽  
Author(s):  
Amaury Hernandez-Aguila ◽  
Mario Garcia-Valdez ◽  
Oscar Castillo
Keyword(s):  
Graphical Representation ◽  
Fuzzy Inference ◽  
Membership Functions ◽  
Fuzzy Inference Systems ◽  
Intuitionistic Fuzzy ◽  
Inference Systems

Air Quality Modeling by Fuzzy Sets and IF-Sets

2011 ◽  
pp. 118-143 ◽  
Author(s):  
Vladimír Olej ◽  
Petr Hájek
Keyword(s):  
Air Quality ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Air Quality Modeling ◽  
Membership Functions ◽  
Fuzzy Inference Systems ◽  
Feature Maps ◽  
Quality Classification ◽  
Quality Modeling ◽  
Inference Systems

The chapter presents a design of parameters for air quality classification of districts into classes according to their pollution. Therefore, the chapter presents basic notions of fuzzy sets introduced by L. A. Zadeh for design hierarchical fuzzy inference systems Mamdani type and IF-sets introduced by K. T. Atanassov for design of hierarchical IF-inference systems Mamdani type. In the next part of the chapter the authors describe air quality modeling by hierarchical fuzzy inference systems, hierarchical IF-inference systems and we analyze the results. Moreover, the chapter describes air quality modeling, the design of membership functions and non-membership functions, if-then rules of individual subsystems and inference mechanism. Further, the authors present basic notions of IF-relations and their determination by Kohonen’s Self-organizing Feature Maps and K-means algorithms and process air quality classification.

FIS/IFIS Modeling in Professional and Collaborative Learning

2015 ◽  
pp. 326-354
Keyword(s):  
Fuzzy Logic ◽  
Collaborative Learning ◽  
Professional Learning ◽  
Fuzzy Inference ◽  
Test Bed ◽  
Fuzzy Inference Systems ◽  
Computer Supported Collaborative Learning ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Logic ◽  
Inference Systems

In this chapter, the capability of the fuzzy inference systems (FISs) to model and provide evaluations in the educational context is further explored through the merits of the intuitionistic fuzzy inference systems (IFISs). The Intuitionistic Fuzzy Logic enables the capture and expression of uncertainty and hesitancy with an IFIS model, thus it extends the fuzzy logic capabilities. In this chapter, the purpose and function of the FIS/IFIS modeling, when embedded in an instructional design (ID), is further examined from Boulding's systemic perspective. Elaborations of the latter provide a framework for handling the complexity of the above interplay and clarify the aim and the role of the presented modeling approaches. The ID and FIS/IFIS modeling upon experimental data from their materialization in two educational cases in the area of professional learning and computer supported collaborative learning, respectively, serve as the test-bed for the potentiality of the presented explorations.

scholarly journals Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Sri Supatmi ◽  
Rongtao Hou ◽  
Irfan Dwiguna Sumitra
Keyword(s):  
Fuzzy Inference ◽  
Flood Event ◽  
Membership Functions ◽  
Fuzzy Inference Systems ◽  
Flood Vulnerability ◽  
Inference System ◽  
Forecasting Accuracy ◽  
Proposed Model ◽  
Inference Systems ◽  
Takagi Sugeno

An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.

scholarly journals Properties of interval type-2 fuzzy sets in decision support systems

2021 ◽  
pp. 75-81
Author(s):  
Alexander Zakovorotniy ◽  
Artem Kharchenko
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Systems Design ◽  
Membership Functions ◽  
Fuzzy Inference Systems ◽  
Footprint Of Uncertainty ◽  
Expert Assessments ◽  
Interval Type ◽  
Inference Systems

Definitions and methods of designing interval type-2 fuzzy sets in fuzzy inference systems for control problems of complex technical objects in conditions of uncertainty are considered. The main types of uncertainties, that arise when designing fuzzy inference systems and depend on the number of expert assessments, are described. Methods for assessing intra-uncertainty and inter-uncertainty are proposed, taking into account the different number of expert assessments at the stage of determining the types and number of membership functions. Factors influencing the parameters and properties of interval type-2 fuzzy during experimental studies are determined. Such factors include the number of experiments performed, external factors, technical parameters of the control object, and the reliability of the components of the computer system decision support system. The properties of the lower and upper membership functions of interval type-2 fuzzy sets are investigated on the example of the Gaussian membership function, which is one of the most used in the problems of fuzzy inference systems design. The main features and differences in the methods of determining the lower and upper membership functions of interval type-2 fuzzy sets for different types of uncertainties are taken into account. Methods for determining the footprint of uncertainty, as well as the dependence of its size on the number of expert assessments, are considered. The footprint of uncertainty is characterized by the lower and upper membership functions, and its size directly affects the accuracy of the obtained solutions. Methods for determining interval type-2 fuzzy sets using regulation factors of membership function parameters for intra-uncertainty and weighting factors of membership functions for inter-uncertainties have been developed. The regulation factor of the function parameters can be used to describe the lower and upper membership functions while determining the size of the footprint of uncertainty. Complex interval type-2 sets are determined to take into account inter-uncertainties in the problems of fuzzy inference systems design.

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