scholarly journals Connecting Fuzzifying Topologies and Generalized Ideals by Means of Fuzzy Preorders

2009 ◽  
Vol 2009 ◽  
pp. 1-16
Author(s):  
G. Elsalamony
Keyword(s):  
Generalized Ideals ◽  
Fuzzy Subsets

The present paper investigates the relations between fuzzifying topologies and generalized ideals of fuzzy subsets, as well as constructing generalized ideals and fuzzifying topologies by means of fuzzy preorders. Furthermore, a construction of generalized ideals from preideals, and vice versa, is obtained. As a particular consequence of the results in this paper, a construction of fuzzifying topology generated by generalized ideals of fuzzy subsets via a givenI-topology is given. The notion ofσ-generalized ideal is introduced and hence everyσ-generalized ideal is shown to be a fuzzifying topology induced by some fuzzy preorder.

The Process of Medical Diagnosis: Routes of Mathematical Investigations

1978 ◽  
Vol 17 (01) ◽  
pp. 1-10 ◽  
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Decision Theory ◽  
Medical Diagnosis ◽  
Pattern Analysis ◽  
Logical Probability ◽  
Inquiry Process ◽  
Fuzzy Subsets

This paper is an analysis of the most important mathematical aspects of medical diagnosis: logical probability, rationality and decision theory, gambling models, pattern analysis, hazy and fuzzy subsets theory and, finally, the stochastic inquiry process.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

Role of Operator Semirings in Characterizing Γ-semirings in Terms of Fuzzy Subsets

2012 ◽  
Vol 4 (3) ◽  
pp. 261-272 ◽  
Author(s):  
Sujit Kumar Sardar ◽  
Sarbani Goswami ◽  
Y. B. Jun
Keyword(s):  
Fuzzy Subsets

Ophthalmological prognosis via fuzzy subsets

1982 ◽  
Vol 7 (2) ◽  
pp. 123-138 ◽  
Author(s):  
Olufemi O Oguntade ◽  
Paul E Beaumont
Keyword(s):  
Fuzzy Subsets

On strongly convex L-fuzzy subsets of an ordered semigroup

2017 ◽  
Vol 32 (3) ◽  
pp. 1735-1744 ◽  
Author(s):  
Xiaokun Huang ◽  
Qingguo Li
Keyword(s):  
Ordered Semigroup ◽  
Strongly Convex ◽  
Fuzzy Subsets

scholarly journals Union of Fuzzy Sub - Quadradic Groups Fuzzy Sub - Pendant Groups and Fuzzy Sub - N Groups

2019 ◽  
pp. 303-311
Author(s):  
S. Chandrasekaran ◽  
N. Deepica
Keyword(s):  
Pendant Group ◽  
Definition Of ◽  
Quadratic Group ◽  
Fuzzy Subsets

In this paper, union of Fuzzy subsets, Fuzzy sub – Quadratic group, fuzzy sub - Pendant group and Fuzzy sub–N groups are discussed. Moreover, some properties and theorems based on these have been derived and derive the definitions of Union of Fuzzy sub – Quadratic group, definitions of Union of fuzzy sub- Pendant group and soon definitions of Union of Fuzzy sub–N groups, and derive the definitions of Fuzzy Quadratic group, definitions of fuzzy Pendant group and soon definitions of Fuzzy N group and definition of Quadratic group, definitions of Pendant group and soon definitions of N group.

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