A framework for designing cognitive trajectory controllers using genetically evolved interval type‐2 fuzzy cognitive maps

2021 ◽  
Author(s):  
Abdollah Amirkhani ◽  
Masoud Shirzadeh ◽  
Tufan Kumbasar ◽  
Behrooz Mashadi
Keyword(s):  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Cognitive Trajectory ◽  
Interval Type

Understanding and Modeling Information Dominance in Battle Management: Applications of Fuzzy Cognitive Maps

1998 ◽  
Author(s):  
Karl Perusich ◽  
Michael D. McNeese
Keyword(s):  
Cognitive Maps ◽  
Fuzzy Cognitive Maps

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.

Modeling of the functional structure of innovative coal mining enterprises using fuzzy cognitive maps and support systems for project decisions

2019 ◽  
Vol 12 (47) ◽  
pp. 68-73
Author(s):  
A.A. Gurkov ◽  
◽  
N.L. Razumnik ◽  
E.V. Horn ◽  
E.N. Yakunchikov ◽  
...  
Keyword(s):  
Coal Mining ◽  
Support Systems ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Functional Structure

Interval Type 2 Fuzzy Logic Based Multifocus Image Fusion

2018 ◽  
Vol 6 (1) ◽  
pp. 220-227
Author(s):  
A.N. Myna ◽  
◽  
J. Prakash ◽  
Keyword(s):  
Fuzzy Logic ◽  
Image Fusion ◽  
Interval Type

FUZZY COGNITIVE MAPS EMBEDDED IN A LOW COST AUTONOMOUS MOBILE ROBOT

2019 ◽  
Author(s):  
Márcio Mendonça ◽  
Guilherme Bender Sartori ◽  
Lucas Botoni de Souza ◽  
Giovanni Bruno Marquini Ribeiro
Keyword(s):  
Mobile Robot ◽  
Low Cost ◽  
Cognitive Maps ◽  
Fuzzy Cognitive Maps ◽  
Autonomous Mobile Robot

A dynamic fuzzification approach for interval type-2 membership function development: case study for QoS planning

2021 ◽  
Author(s):  
Diana Kalibatiene ◽  
Jolanta Miliauskaitė
Keyword(s):  
Membership Function ◽  
Interval Type
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