Genetic algorithm-aided dynamic fuzzy rule interpolation

2014 ◽  
Author(s):  
Nitin Naik ◽  
Ren Diao ◽  
Qiang Shen
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rule

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

An Intelligent Process Development Using Fusion of Genetic Algorithm with Fuzzy Logic

2015 ◽  
pp. 44-81 ◽  
Author(s):  
Kunjal Bharatkumar Mankad
Keyword(s):  
Genetic Algorithm ◽  
Process Development ◽  
Intelligent System ◽  
Mathematical Formulation ◽  
Research Work ◽  
Fuzzy Rule ◽  
Genetic Operators ◽  
Rule Based System ◽  
Intelligent Process

Intelligent System (IS) can be defined as the system that incorporates intelligence into applications being handled by machines. The chapter extensively discusses the role of Genetic Algorithm (GA) in the search and optimization process along with discussing applications developed so far. A very detailed discussion on the Fuzzy Rule-Based System is presented along with major applications developed in different domains. The chapter presents algorithm of implementing intelligent procedure to decide whether a patient is prone to heart disease or not. The procedure evolves solutions using genetic operators and provides its decision automatically. The chapter presents discussion on the results achieved as a result of prototypical implementation of the evolutionary fuzzy hybrid model. The significant advantage of the presented research work is that applications that do not have any mathematical formulation and still demand optimization can be easily solved using the designed approach.

A fuzzy rule induction method using genetic algorithm

1996 ◽  
Vol 18 (2-3) ◽  
pp. 135-145 ◽  
Author(s):  
Toshio Tsuchiya ◽  
Tatsushi Maeda ◽  
Yukihiro Matsubara ◽  
Mitsuo Nagamachi
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rule ◽  
Rule Induction ◽  
Induction Method

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

2013 ◽  
Vol 274 ◽  
pp. 345-349 ◽  
Author(s):  
Mei Lan Zhou ◽  
Deng Ke Lu ◽  
Wei Min Li ◽  
Hui Feng Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Membership Functions ◽  
Logic Control

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems

2012 ◽  
Vol 12 (2) ◽  
pp. 800-806 ◽  
Author(s):  
Emel Kızılkaya Aydogan ◽  
Ismail Karaoglan ◽  
Panos M. Pardalos
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Fuzzy Rule ◽  
Classification Systems ◽  
High Dimensional ◽  
Rule Based
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