Approximate bisimulations and state reduction of fuzzy automata under fuzzy similarity measures

2020 ◽  
Vol 391 ◽  
pp. 72-95
Author(s):  
Chao Yang ◽  
Yongming Li
Keyword(s):  
Similarity Measures ◽  
State Reduction ◽  
Fuzzy Similarity ◽  
Fuzzy Automata

scholarly journals Fuzzy logic in air pollution: Revisited

2014 ◽  
Vol 2 ◽  
pp. 1-5
Author(s):  
A. Deshpande
Keyword(s):  
Air Pollution ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Epistemic Uncertainty ◽  
Similarity Measures ◽  
Epidemiological Studies ◽  
Fuzzy Similarity ◽  
Parametric Data ◽  
Numeric Data ◽  
Pollution Exposure

In everyday life and field, people mostly deal with concepts that involve factors that defy classification into crisp sets. The decisions people usually make are perceptions without rigorous analysis of numeric data. Like in other field of studies, there may exist imprecision in air quality parametric data collected and in the perception made by air quality experts in defining these parameters in linguistic terms such as: very good, good, poor. This is the reason why over the past few decades, soft computing tools such as fuzzy logic based methods, neural networks, and genetic algorithms have had significant and growing impacts to deal with aleatory as well as epistemic uncertainty in air quality related issues. This paper has highlighted mathematical preliminaries of air pollution studies like Similarity Measures (Cosine Amplitude Method), Fuzzy to Crisp Conversion (Alpha cut method), Fuzzy c Mean Clustering, Zadeh-Deshpande (ZD) Approach and linguistic description of air quality. Similarly, the applications of fuzzy similarity measures and fuzzy c mean clustering with defined possibility (- cut) levels in case air pollution studies for Delhi, India have been reflected. Though the approach of using fuzzy logic in pollution studies are not of common practice, the comprehensive approach that involves air pollution exposure surveys, toxicological data, and epidemiological studies coupled with fuzzy modeling will go a long way toward resolving some of the divisiveness and controversy in the current regulatory paradigm.

Efficacy of Fuzzy-Stat Modelling in Classification of Gynaecologists and Patients

2016 ◽  
Vol 25 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Anjali Sardesai ◽  
Vilas Kharat ◽  
Pradip Sambarey ◽  
Ashok Deshpande
Keyword(s):  
Medical Science ◽  
Similarity Measures ◽  
Disease Diagnosis ◽  
Diagnostic Process ◽  
Mathematical Framework ◽  
Domain Experts ◽  
Binary Logic ◽  
Fuzzy Similarity ◽  
Medical Diagnostic ◽  
Inference Systems

AbstractFuzzy logic-based inference systems depend on the domain experts’ perceptions, which are intrinsically imprecise/vague/fuzzy. The perceptions of more than one expert are needed in the decision-making process. Therefore, there is a need to study the similarity between the experts using a mathematical framework. Classical mathematical models simulating the medical diagnostic process are usually either logical or probabilistic, wherein the concept of partial belief is not considered. Except in a few cases, binary logic is too unrealistic to apply to medical diagnosis. Another important factor in medical science is the patient-symptom relationship, which influences the disease diagnosis. In summary, the following two issues stand out: (i) Do experts agree with one another in arriving at the same diagnostic labels? (ii) Based on the symptom-patient relationship, can patients be classified? The authors have tried to explore the possibility of using fuzzy similarity measures and also Gower’s coefficient in classifying gynaecologists and patients. The comparative evaluation infers that the efficacy of two-valued binary logic-based Gower’s coefficient is low.

scholarly journals Fuzzy similarity measures as a basis for the selection of examiners

2015 ◽  
Vol 2015 (3) ◽  
pp. 245-258 ◽  
Author(s):  
Satish S. Salunkhe ◽  
Yashwant Joshi ◽  
Ashok Deshpande
Keyword(s):  
Similarity Measures ◽  
Fuzzy Similarity ◽  
Selection Of

scholarly journals Weakly linear systems of fuzzy relation inequalities and their applications: A brief survey

Filomat ◽  
2012 ◽  
Vol 26 (2) ◽  
pp. 207-241 ◽  
Author(s):  
Jelena Ignjatovic ◽  
Miroslav Ciric
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Linear Systems ◽  
Fuzzy Relation ◽  
General Aspect ◽  
State Reduction ◽  
Fuzzy Relation Equations ◽  
The Social ◽  
Fuzzy Automata ◽  
Weakly Linear

Weakly linear systems of fuzzy relation inequalities and equations have recently emerged from research in the theory of fuzzy automata. From the general aspect of the theory of fuzzy relation equations and inequalities homogeneous and heterogeneousweakly linear systems have been discussed in two recent papers. Here we give a brief overview of the main results from these two papers, as well as from a series of papers on applications of weakly linear systems in the state reduction of fuzzy automata, the study of simulation, bisimulation and equivalence of fuzzy automata, and in the social network analysis. Especially, we present algorithms for computing the greatest solutions to weakly linear systems.

Sign in / Sign up

Export Citation Format

Share Document