Defuzzification methods in intuitionistic fuzzy inference systems of Takagi-Sugeno type: The case of corporate bankruptcy prediction

2014 ◽  
Author(s):  
Petr Hajek ◽  
Vladimir Olej
Keyword(s):  
Fuzzy Inference ◽  
Bankruptcy Prediction ◽  
Fuzzy Inference Systems ◽  
Corporate Bankruptcy ◽  
Intuitionistic Fuzzy ◽  
Inference Systems ◽  
Takagi Sugeno ◽  
Corporate Bankruptcy Prediction

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.

Investigation of Hierarchical Fuzzy Inference Systems, when Obtaining Integral Estimates of the Analyzed Objects

2021 ◽  
Vol 27 (11) ◽  
pp. 582-591
Author(s):  
A. A. Sorokin ◽  
Keyword(s):  
Fuzzy Inference ◽  
Training Sample ◽  
Strong Increase ◽  
Hierarchical Systems ◽  
Fuzzy Inference Systems ◽  
Machine Learning Methods ◽  
Output Pattern ◽  
Input Variables ◽  
Inference Systems ◽  
Takagi Sugeno

The purpose of this paper is to study the patterns of the formation of output values in hierarchical systems offuzzy inference. Hierarchical fuzzy inference systems (HFIS) are used to aggregate heterogeneous parameters during the assessment of the state of various elements of complex systems. The use of HFIS allows avoiding the "curse" of the dimension associated with a strong increase in the number and complication of the structure of the production rule, which is characteristic of conventional fuzzy inference systems (FIS), which aggregate the results of interaction of different values of input variables in one knowledge base. As part of the research, numerical experiments were carried out to study the features of the formation of output patterns in HFIS, based on FIS using the Mamdani and Takagi-Sugeno algorithms. As a result of the experiment, it was shown that the output values of the studied HFIS tend to be grouped in the region of fixed values, and the output pattern itself acquires a stepwise character. The revealed property allows using HFIS to distribute the objects of the analyzed sample into groups of states. This property can be used to solve problems of distributing objects into groups in conditions when it is difficult to form a training sample for machine learning methods, but at the same time there is knowledge of the expert group about the features of the functioning of the object of research. Additionally, the paper investigates the features of the formation of output patterns depending on the parameters of the membership functions describing the input variables in HFIS, which are based on FIS using the Mamdani algorithm and HFIS, which are based on FIS using the Takagi-Sugeno algorithm.

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.

Sign in / Sign up

Export Citation Format

Share Document