Rule base identification toolbox for fuzzy controllers

Author(s):  
Zsolt Csaba Johanyak ◽  
Piroska Ailer
Keyword(s):  
2009 ◽  
Vol 36 (4) ◽  
pp. 8476-8486 ◽  
Author(s):  
Hakkı Murat Genc ◽  
Engin Yesil ◽  
Ibrahim Eksin ◽  
Mujde Guzelkaya ◽  
Ozgur Aydın Tekin

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.


2017 ◽  
Vol 0 (4) ◽  
Author(s):  
Oleksiy V. Kozlov ◽  
Galyna V. Kondratenko ◽  
Yuriy P. Kondratenko

2016 ◽  
Author(s):  
Leonardo G. Melo ◽  
Luís A. Lucas ◽  
Myriam R. Delgado

1993 ◽  
Vol 28 (3-5) ◽  
pp. 625-634 ◽  
Author(s):  
D. A. Ford ◽  
A. P. Kruzic ◽  
R. L. Doneker

AWARDS is a rule-based program that uses artificial intelligence techniques. It predicts the potential for fields receiving agricultural waste applications to degrade water quality. Input data required by AWARDS include the physical features, management practices, and crop nutrient needs for all fields scheduled to receive these nutrients. Based on a series of rules AWARDS analyzes the data and categorizes each field as acceptable or unacceptable for agricultural waste applications. The acceptable fields are then ranked according to their potential for pollutant loading. To evaluate the validity of the AWARDS field ranking system, it was compared to pollutant loading output from GLEAMS, a complex computer model. GLEAMS simulated the characteristics of each field ranked by AWARDS. Comparison of the AWARDS field ranking to the GLEAMS pollutant loading was favorable where ground water and both surface and ground water were to be protected and less favorable where surface water was to be protected. The rule base in AWARDS may need to be refined to provide more reasonable results where surface water is the resource of concern.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  

2021 ◽  
pp. 113558
Author(s):  
You Cao ◽  
Zhijie Zhou ◽  
Changhua Hu ◽  
Shuaiwen Tang ◽  
Jie Wang

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