Design of Information Granules-Based Fuzzy Systems Using Clustering Algorithm and Genetic Optimization

2005 ◽  
pp. 1142-1145
Author(s):  
Sung-Kwun Oh ◽  
Keon-Jun Park ◽  
Witold Pedrycz ◽  
Tae-Chon Ahn
Keyword(s):  
Fuzzy Systems ◽  
Clustering Algorithm ◽  
Genetic Optimization ◽  
Information Granules

scholarly journals Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Leticia Cervantes ◽  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Ricardo Martinez-Soto
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Flight Control ◽  
Fuzzy Systems ◽  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Control Problems ◽  
Genetic Optimization ◽  
Two Parameters

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

A Study of Fuzzy Hysteresis Modeling and Control of a Shape Memory Wire Actuator

2007 ◽  
Author(s):  
Ali Reza Mehrabian ◽  
S. Vahid Hashemi ◽  
Eric Williams ◽  
Mohammad Elahinia
Keyword(s):  
Shape Memory ◽  
Fuzzy Systems ◽  
Clustering Algorithm ◽  
Nonlinear Behavior ◽  
Fuzzy Modeling ◽  
Inverse Model ◽  
Training Data ◽  
Modeling And Control ◽  
Sma Actuators ◽  
And Control

This paper describes the development of fuzzy systems for modeling the hysteresis behavior of shape memory alloy (SMA) actuators. Due to their simplicity and ease of actuation, SMA actuators are very attractive for applications such as miniature robots for micro manufacturing. However, SMAs have not been widely used for motion control applications due to their nonlinear behavior and control difficulties. One approach to design a position controller for SMA systems is to employ an inverse-model of the system in the control loop to compensate the hysteresis properties of the material. Fuzzy systems, due to their nonlinear learning and adaptation abilities, are good candidates for obtaining inverse-models. In this paper two fuzzy modeling approaches are employed and compared to develop a model for a SMA wire actuator. A set of experiments are conducted to generate the training data. The test stand includes a Nickel-Titanium (TiNi) SMA wire, a position sensor, a bias spring and a current amplifier. By comparing the performance of the two employed fuzzy modeling techniques, it is revealed that the approach based on fuzzy Gustafson-Kessel (GK) clustering shows a better performance in the modeling of the hysteresis in the SMA wire. Thus, GK clustering algorithm is employed to develop the inverse-model for the SMA. The reported results demonstrate the ability of the employed fuzzy algorithm for modeling the hysteresis in the system, and the merits of the introduced inverse-model in the control of the position of the SMA.

Genetic Optimization of Fuzzy Systems for the Classification of Treated Water Quality

2018 ◽  
pp. 248-260
Author(s):  
Itzel G. Gaytan-Reyes ◽  
Nohe R. Cazarez-Castro ◽  
Selene L. Cardenas-Maciel ◽  
David A. Lara-Ochoa ◽  
Armando Martinez-Graciliano
Keyword(s):  
Water Quality ◽  
Fuzzy Systems ◽  
Genetic Optimization ◽  
Treated Water

An Efficient Solution of Real-Time Fuzzy Regression Analysis to Information Granules Problem

2012 ◽  
Vol 16 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Azizul Azhar Ramli ◽  
◽  
Junzo Watada ◽  
Witold Pedrycz ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Convex Hull ◽  
Real Time ◽  
Clustering Algorithm ◽  
System Modeling ◽  
Computing Time ◽  
Fuzzy Regression ◽  
Information Granules ◽  
Hull Structure

Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach being regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing helps us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, we emphasize a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.

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