Subjective decision making using type-2 fuzzy logic advisor

2009 ◽  
Author(s):  
M. A. Owais
Keyword(s):  
Decision Making ◽  
Fuzzy Logic

Recent Advancement in Fuzzy System

2014 ◽  
pp. 158-168
Author(s):  
İ. Burhan Türkşen ◽  
İbrahim Özkan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Interdisciplinary Research ◽  
Decision Process ◽  
Fuzzy System ◽  
Research Field ◽  
Decision Under Uncertainty ◽  
Recent Advancement ◽  
Human Decision

Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative that best suites our needs. This process generally includes several areas of research including but not limited to Economics, Psychology, Philosophy, Mathematics, Statistics, etc. In this chapter the authors attempt to create a framework for uncertainties which surrounds the environment where human decision making takes place. For this purpose, the authors discuss how one ought to handle uncertainties within Fuzzy Logic. Furthermore, they present recent advances in Type 2 fuzzy system studies.

Recent Advancement in Fuzzy System

2015 ◽  
pp. 437-447
Author(s):  
İ. Burhan Türkşen ◽  
İbrahim Özkan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Interdisciplinary Research ◽  
Decision Process ◽  
Fuzzy System ◽  
Research Field ◽  
Decision Under Uncertainty ◽  
Recent Advancement ◽  
Human Decision

Decision under uncertainty is an active interdisciplinary research field. A decision process is generally identified as the action of choosing an alternative that best suites our needs. This process generally includes several areas of research including but not limited to Economics, Psychology, Philosophy, Mathematics, Statistics, etc. In this chapter the authors attempt to create a framework for uncertainties which surrounds the environment where human decision making takes place. For this purpose, the authors discuss how one ought to handle uncertainties within Fuzzy Logic. Furthermore, they present recent advances in Type 2 fuzzy system studies.

scholarly journals A Big-Bang Big-Crunch Optimized General Type-2 Fuzzy Logic Approach for Multi-Criteria Group Decision Making

2013 ◽  
Vol 3 (2) ◽  
pp. 117-132 ◽  
Author(s):  
Syibrah Naim ◽  
Hani Hagras
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Group Decision Making ◽  
General Type ◽  
Group Decision ◽  
Big Bang ◽  
Decision Makers ◽  
Big Crunch ◽  
Interval Type

Abstract Multi-Criteria Group Decision Making (MCGDM) aims to find a unique agreement from a number of decision makers/users by evaluating the uncertainty in judgments. In this paper, we present a General Type-2 Fuzzy Logic based approach for MCGDM (GFLMCGDM). The proposed system aims to handle the high levels of uncertainties which exist due to the varying Decision Makers’ (DMs) judgments and the vagueness of the appraisal. In order to find the optimal parameters of the general type-2 fuzzy sets, we employed the Big Bang-Big Crunch (BB-BC) optimization. The aggregation operation in the proposed method aggregates the various DMs opinions which allow handling the disagreements of DMs’ opinions into a unique approval. We present results from an application for the selection of reading lighting level in an intelligent environment. We carried out various experiments in the intelligent apartment (iSpace) located at the University of Essex. We found that the proposed GFL-MCGDM effectively handle the uncertainties between the various decision makers which resulted in producing outputs which better agreed with the users’ decision compared to type 1 and interval type 2 fuzzy based systems.

Overview of Type-2 Fuzzy Logic Systems

2012 ◽  
Vol 2 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Systems ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Systems ◽  
Logic Systems

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead of a crisp number. Type-2 fuzzy logic systems (T2FLSs) offer opportunity to model levels of uncertainty which traditional fuzzy logic type1 struggles. This extra dimension gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets. A type-1 fuzzy logic system (T1FLSs) inference produces a T1FS and the result of defuzzification of the T1FS, a crisp number, whereas a T2FLS inference produces a type-2 fuzzy set, its type-reduced fuzzy set which is a T1FS and the defuzzification of the type-1 fuzzy set. The type-reduced fuzzy set output gives decision-making flexibilities. Thus, FLSs using T2FS provide the capability of handling a higher level of uncertainty and provide a number of missing components that have held back successful deployment of fuzzy systems in decision making.

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