New genetic-based approach to generate fuzzy rules from numerical data

2002 ◽  
Author(s):  
Jun Zhu ◽  
Run-Sheng Yang ◽  
Shi-Yu Sun
Keyword(s):  
Numerical Data ◽  
Fuzzy Rules

HYSTERESIS CHARACTERIZATION BY A FUZZY LEARNING ALGORITHM

1994 ◽  
Vol 02 (03) ◽  
pp. 351-360
Author(s):  
EDGE C. YEH ◽  
SHAO HOW LU
Keyword(s):  
Fuzzy Sets ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Numerical Data ◽  
Underlying Mechanism ◽  
Fuzzy Rules ◽  
Hysteresis Phenomenon ◽  
Input Output ◽  
Fuzzy Learning

In this paper, the hysteresis characterization in fuzzy spaces is presented by utilizing a fuzzy learning algorithm to generate fuzzy rules automatically from numerical data. The hysteresis phenomenon is first described to analyze its underlying mechanism. Then a fuzzy learning algorithm is presented to learn the hysteresis phenomenon and is used for predicting a simple hysteresis phenomenon. The results of learning are illustrated by mesh plots and input-output relation plots. Furthermore, the dependency of prediction accuracy on the number of fuzzy sets is studied. The method provides a useful tool to model the hysteresis phenomenon in fuzzy spaces.

Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm

1995 ◽  
Vol 7 (1) ◽  
pp. 29-35
Author(s):  
Toshio Fukuda ◽  
◽  
Yasuhisa Hasegawa ◽  
Koji Shimojima
Keyword(s):  
Genetic Algorithm ◽  
Hierarchical Structure ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Numerical Data ◽  
Fuzzy Rules ◽  
Input And Output ◽  
The Hierarchical Structure ◽  
Input Variables ◽  
The Relationship

This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relationship among input and output variables. The proposed method can organize the suitable hierarchical structure for the relationship among input and output variables in teaching numerical data. It is based on the Genetic Algorithm with an evaluation function as a strategy that adopts a system with fewer fuzzy rules and more accurate outputs. The proposed method is applied to the approximation problems of multi-dimensional nonlinear functions in order to demonstrate its effectiveness.

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