A low-complexity fuzzy-logic-control-based vertical handoff decision algorithm with rough set theory

2013 ◽  
Author(s):  
Yanan Feng ◽  
Lei Xie ◽  
Huifang Chen ◽  
Kuang Wang
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Logic Control ◽  
Rough Set Theory ◽  
Low Complexity ◽  
Vertical Handoff ◽  
Decision Algorithm ◽  
Logic Control

scholarly journals Topology in the Alternative Set Theory and Rough Sets via Fuzzy Type Theory

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 432 ◽  
Author(s):  
Vilém Novák
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Rough Sets ◽  
Type Theory ◽  
Rough Set Theory ◽  
Basic Concepts ◽  
Alternative Set

In this paper, we will visit Rough Set Theory and the Alternative Set Theory (AST) and elaborate a few selected concepts of them using the means of higher-order fuzzy logic (this is usually called Fuzzy Type Theory). We will show that the basic notions of rough set theory have already been included in AST. Using fuzzy type theory, we generalize basic concepts of rough set theory and the topological concepts of AST to become the concepts of the fuzzy set theory. We will give mostly syntactic proofs of the main properties and relations among all the considered concepts, thus showing that they are universally valid.

PC Based Educational Tool for a Switched Reluctance Drive with Fuzzy Logic

2003 ◽  
Vol 40 (3) ◽  
pp. 208-219 ◽  
Author(s):  
Cetin Elmas ◽  
M. Ali Akcayol
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Logic Control ◽  
Drive System ◽  
Educational Tool ◽  
Flux Linkage ◽  
Switched Reluctance ◽  
Logic Control

This paper introduces a PC based educational tool for a switched reluctance drive (SRD) with fuzzy logic which is prepared for undergraduate and graduate level students. The paper first describes how the switched reluctance drive system works. Then details are given of how to estimate the flux linkage by using fuzzy set theory. Finally an application of fuzzy logic control to an SRM drive is presented.

Rough Set Theory and Fuzzy Logic Based Warehousing of Heterogeneous Clinical Databases

2017 ◽  
Vol 25 (03) ◽  
pp. 385-408 ◽  
Author(s):  
R. Saravana Kumar ◽  
G. Tholkappia Arasu
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Second Phase ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Clinical Databases ◽  
Efficient Data

Large amounts of data about the patients with their medical conditions are presented in the Medical databases. Analyzing all these databases is one of the difficult tasks in the medical environment. In order to warehouse all these databases and to analyze the patient’s condition, we need an efficient data mining technique. In this paper, an efficient data mining technique for warehousing clinical databases using Rough Set Theory (RST) and Fuzzy Logic is proposed. Our proposed methodology contains two phases – (i) Clustering and (ii) Classification. In the first phase, Rough Set Theory is used for clustering. Clustering is one of the data mining techniques for warehousing the heterogeneous data bases. Clustering technique is used to group data that have similar characteristics in the same cluster and also to group the data that have dissimilar characteristics with other clusters. After clustering the data, similar objects will be clustered in one cluster and the dissimilar objects will be clustered under another cluster. The RST can be reduced the complexity. Then in the second phase, these clusters are classified using Fuzzy Logic. Normally, Classification with Fuzzy Logic is generated more number of rules. Since the RST is utilized in our work, the classification using Fuzzy can be done with less amount of complexity. The proposed approach is evaluated using various clinical related databases from heart disease datasets – Cleveland, Switzerland and Hungarian. The performance analysis is based on Sensitivity, Specificity and Accuracy with different cluster numbers. The experimentation results show that our proposed methodology provides better accuracy result.

A Hybrid Protocol using Fuzzy Logic and Rough Set Theory for Target Coverage

2020 ◽  
Vol 13 ◽  
Author(s):  
Pooja Chaturvedi ◽  
A K Daniel
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Rough Set ◽  
Network Performance ◽  
Processing Time ◽  
Rough Set Theory ◽  
Set Cover ◽  
Target Coverage ◽  
Rule Set ◽  
Trust Calculation

Background: Target coverage is considered as a significant problem in the area of wireless sensor networks which usually aims at monitoring a given set of targets with the required confidence level so that network lifetime can be enhanced while considering the constraints of the resources. Objective: To maximize the lifetime of the sensor network and minimize the overhead involved in the scheduling approach, such that the pre specified set of targets is monitored for longer duration with the required confidence level. Methods: The paper uses a fuzzy logic system based on Mamdani inference in which the node status to remain in the active state is determined on the basis of coverage probability, trust values and node contribution. The rule set for determining the set cover is optimized by using the rough set theory which aims to determine the node validity for the trust calculation. Results: The results show that the proposed approach improved the network performance in terms of processing time, throughput and energy conservation by a factor of 50%, 74% and 74% respectively as compared to the existing approaches. Conclusion: The paper proposes a scheduling strategy of the nodes for target coverage as an enhancement to the energy efficient coverage protocol (EECP) on the basis of rough set theory. The rule set for determining the set cover is optimized by using the rough set theory so that the network performance is improved in terms of the processing time, throughput and energy consumption. Current and Future Developments: In this paper we have optimized the performance of EECP protocol by considering the concept of Rough Set Theory, which determined the validity of the nodes in the trust calculation. We have evaluated the performance through a number of simulations and the results show the comparative improvement in terms of the processing time, throughput and energy conservation. To utilize the unutilized nodes in the current round for the coverage enhancement is our future work. We also aim to study the effects of heterogeneity on the performance of the proposed protocol in future. Declaration: The authors declare that the research article is author’s original work and have not been sent/ published elsewhere.

On Correctness of Decision Algorithms in Information Systems

1988 ◽  
Vol 11 (3) ◽  
pp. 219-239
Author(s):  
Anita Wasilewska
Keyword(s):  
Information System ◽  
Information Systems ◽  
Decision Rule ◽  
Direct Product ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Algorithm ◽  
Decision Algorithms

The concept of an information system, with manipulation based on the rough set theory, was introduced by Pawlak in 1982. The information system is defined by its set of objects, set of attributes, set of values of attributes, and a function, which maps the direct product of the first two sets onto the set of values of attributes. We introduce here, after [Pawlak 1985(1)], concepts of the decision rule, decision algorithm, static learning and describe the automatic procedures of deciding whether a given decision rule or decision algorithm is correct.

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