Influence of different fuzzy operators on analytical structure and variable gains of typical interval type-2 fuzzy PI controller

2020 ◽  
Vol 39 (3) ◽  
pp. 4319-4329
Author(s):  
Haibo Zhou ◽  
Chaolong Zhang ◽  
Shuaixia Tan ◽  
Yu Dai ◽  
Ji’an Duan ◽  
...  
Keyword(s):  
Pi Controller ◽  
Real Time Control ◽  
Fuzzy Operators ◽  
Fuzzy Operator ◽  
Analytical Structure ◽  
Product Operator ◽  
Pi Controllers ◽  
Fuzzy Pi Controller ◽  
Interval Type

The fuzzy operator is one of the most important elements affecting the control performance of interval type-2 (IT2) fuzzy proportional-integral (PI) controllers. At present, the most popular fuzzy operators are product fuzzy operator and min() operator. However, the influence of these two different types of fuzzy operators on the IT2 fuzzy PI controllers is not clear. In this research, by studying the derived analytical structure of an IT2 fuzzy PI controller using typical configurations, it is proved mathematically that the variable gains, i.e., proportional and integral gains of typical IT2 fuzzy PI controllers using the min() operator are smaller than those using the product operator. Moreover, the study highlights that unlike the controllers based on the product operator, the controllers based on the min() operator have a simple analytical structure but provide more control laws. Real-time control experiments on a linear motor validate the theoretical results.

scholarly journals SUATU KAJIAN TENTANG OPERATOR-OPERATOR BARU PADA HIMPUNAN KABUR HESITANT (NEW HESITANT FUZZY OPERATORS)

2017 ◽  
Vol 6 (3) ◽  
pp. 106
Author(s):  
WINDY AULIA YUSPA
Keyword(s):  
Fuzzy Operators

Himpunan kabur hesitant adalah alat yang muncul dan berguna untuk berurusan dengan ketidakpastian dan ketidakjelasan. Menariknya, kita dapat menentukan operator baru dan sifat dari himpunan kabur hesitant ini, agar kajian tentang himpunan kabur hesitant lebih berkembang. Pada makalah ini dikaji enam operator-operator baru pada anggota himpunan kabur hesitant (O1, O2, O3, O4, O5, O6) dan sifat-sifat nya. Kata Kunci: Himpunan kabur (FS), himpunan kabur hesitant (HFS), anggota himpunan kabur hesitant (HFE), operator

scholarly journals Exploiting NoSQL Graph Databases and in Memory Architectures for Extracting Graph Structural Data Summaries

2017 ◽  
Vol 25 (01) ◽  
pp. 81-109 ◽  
Author(s):  
Arnaud Castelltort ◽  
Anne Laurent
Keyword(s):  
Relational Databases ◽  
Structural Data ◽  
Scientific Data ◽  
Graph Database ◽  
Graph Databases ◽  
Fuzzy Operators ◽  
Data Volume ◽  
Novel Method ◽  
Memory Architectures ◽  
Data Summaries

NoSQL graph databases have been introduced in recent years for dealing with large collections of graph-based data. Scientific data and social networks are among the best examples of the dramatic increase of the use of such structures. NoSQL repositories allow the management of large amounts of data in order to store and query them. Such data are not structured with a predefined schema as relational databases could be. They are rather composed by nodes and relationships of a certain type. For instance, a node can represent a Person and a relationship Friendship. Retrieving the structure of the graph database is thus of great help to users, for example when they must know how to query the data or to identify relevant data sources for recommender systems. For this reason, this paper introduces methods to retrieve structural summaries. Such structural summaries are extracted at different levels of information from the NoSQL graph database. The expression of the mining queries is facilitated by the use of two frame-works: Fuzzy4S allowing to define fuzzy operators and operations with Scala; Cypherf allowing the use of fuzzy operators and operations in the declarative queries over NoSQL graph databases. We show that extracting such summaries can be impossible with the NoSQL query engines because of the data volume and the complexity of the task of automatic knowledge extraction. A novel method based on in memory architectures is thus introduced. This paper provides the definitions of the summaries with the methods to automatically extract them from NoSQL graph databases only and with the help of in-memory architectures. The benefit of our proposition is demonstrated by experimental results.

Innovative Formalism for Biological Data Analysis

2018 ◽  
pp. 1814-1824
Author(s):  
Calin Ciufudean
Keyword(s):  
Information Technology ◽  
Data Analysis ◽  
Medical Devices ◽  
Fuzzy Set ◽  
Biological Data ◽  
Fuzzy Operators ◽  
Biological Data Analysis ◽  
Biological Signals ◽  
Design And Manufacture ◽  
Gathering Data

Modern medical devices involves information technology (IT) based on electronic structures for data and signals sensing and gathering, data and signals transmission as well as data and signals processing in order to assist and help the medical staff to diagnose, cure and to monitors the evolution of patients. By focusing on biological signals processing we may notice that numerical processing of information delivered by sensors has a significant importance for a fair and optimum design and manufacture of modern medical devices. We consider for this approach fuzzy set as a formalism of analysis of biological signals processing and we propose to be accomplished this goal by developing fuzzy operators for filtering the noise of biological signals measurement. We exemplify this approach on neurological measurements performed with an Electro-Encephalograph (EEG).

Emerging Application of Fuzzy Expert System in Medical Domain

2015 ◽  
pp. 1-20 ◽  
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

Distance and similarity measures for fuzzy operators

2007 ◽  
Vol 177 (11) ◽  
pp. 2336-2348 ◽  
Author(s):  
V BALOPOULOS ◽  
A HATZIMICHAILIDIS ◽  
B PAPADOPOULOS
Keyword(s):  
Similarity Measures ◽  
Fuzzy Operators
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