An approach for environment mapping and control of wall follower cellbot through monocular vision and fuzzy system

Author(s):  
Karoline de M. Farias ◽  
R. Wilson Leal ◽  
Ranulfo P. Bezerra Neto ◽  
Ricardo A. L. Rabelo ◽  
Andre M. Santana
2012 ◽  
Vol 3 ◽  
pp. 707-714 ◽  
Author(s):  
Runchen Yan ◽  
Hong Wang ◽  
Yuzhi Yang ◽  
Huanbing Wei ◽  
Yonggang Wang

2013 ◽  
Vol 30 (3) ◽  
pp. 415-438 ◽  
Author(s):  
Girish Chowdhary ◽  
Eric N. Johnson ◽  
Daniel Magree ◽  
Allen Wu ◽  
Andy Shein

2021 ◽  
Vol 11 (4) ◽  
pp. 1793
Author(s):  
Helbert Espitia ◽  
José Soriano ◽  
Iván Machón ◽  
Hilario López

This document presents some considerations and procedures to design a compact fuzzy system based on Boolean relations. In the design process, a Boolean codification of two elements is extended to a Kleene’s of three elements to perform simplifications for obtaining a compact fuzzy system. The design methodology employed a set of considerations producing equivalent expressions when using Boole and Kleene algebras establishing cases where simplification can be carried out, thus obtaining compact forms. In addition, the development of two compact fuzzy systems based on Boolean relations is shown, presenting its application for the identification of a nonlinear plant and the control of a hydraulic system where it can be seen that compact structures describes satisfactory performance for both identification and control when using algorithms for optimizing the parameters of the compact fuzzy systems. Finally, the applications where compact fuzzy systems are based on Boolean relationships are discussed allowing the observation of other scenarios where these structures can be used.


2014 ◽  
Vol 4 (3) ◽  
pp. 215-225 ◽  
Author(s):  
Edgar Camargo ◽  
Jose Aguilar

Abstract In this work is presented a hybrid intelligent model of supervision based on Evolutionary Computation and Fuzzy Systems to improve the performance of the Oil Industry, which is used for Operational Diagnosis in petroleum wells based on the gas lift (GL) method. The model is composed by two parts: a Multilayer Fuzzy System to identify the operational scenarios in an oil well and a genetic algorithm to maximize the production of oil and minimize the flow of gas injection, based on the restrictions of the process and the operational cost of production. Additionally, the first layers of the Multilayer Fuzzy System have specific tasks: the detection of operational failures, and the identification of the rate of gas that the well requires for production. In this way, our hybrid intelligent model implements supervision and control tasks.


2015 ◽  
Vol 16 (4) ◽  
pp. 581-589
Author(s):  
Bong-kyu Cheon ◽  
Jeong-ho Kim ◽  
Chan-oh Min ◽  
Dong-in Han ◽  
Kyeum-rae Cho ◽  
...  

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