An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules based on fuzzy clustering method

2002 ◽  
Author(s):  
Y. Shi ◽  
M. Mizumoto
Keyword(s):  
Fuzzy Clustering ◽  
Learning Algorithm ◽  
Fuzzy Rules ◽  
Clustering Method ◽  
Neuro Fuzzy ◽  
Fuzzy Clustering Method ◽  
Fuzzy Learning

Quaternion neuro-fuzzy learning algorithm for generation of fuzzy rules

2016 ◽  
Vol 216 ◽  
pp. 638-648 ◽  
Author(s):  
Ryusuke Hata ◽  
Md. Monirul Islam ◽  
Kazuyuki Murase
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Rules ◽  
Neuro Fuzzy ◽  
Fuzzy Learning

scholarly journals Introducing a software for innovative neuro-fuzzy clustering method named NFCMR

2018 ◽  
Vol 8 (2) ◽  
pp. 62-69 ◽  
Author(s):  
Aref Shirazi ◽  
Adel Shirazy ◽  
Shahab Saki ◽  
Ardeshir Hezarkhani
Keyword(s):  
Neural Network ◽  
Fuzzy Clustering ◽  
Mineral Exploration ◽  
General Regression Neural Network ◽  
Clustering Method ◽  
Neuro Fuzzy ◽  
Fuzzy Clustering Method ◽  
Fuzzy C Means Clustering ◽  
General Regression

An innovative neural-fuzzy clustering method is for predicting cluster (anomaly / background) of each new sample with the probability of its presence. This method which is a combination of the Fuzzy C-Means clustering method (FCM) and the General Regression Neural Network (GRNN), is an attempt to first divide the samples in the region by fuzzy method with the probability of being in each cluster and then with the results of this Practice, the artificial neural network is trained, and can analyze the new data entered in the region with the probable percentage of the clusters. More clearly, after a full mineral exploration, the sample can be attributed to a certain probable percentage of anomalies. To test the accuracy of this clustering in the form of the theory alone, a case study was conducted on the results of the analysis of regional alluvial sediments data in Birjand, IRAN, which resulted in satisfactory results. This software is written in MATLAB and its first application in mining engineering. This algorithm can be used in other similar applications in various sciences.

scholarly journals A Method of Fuzzy Rules Generation Based on Neuro-Fuzzy Learning Algorithm

1996 ◽  
Vol 8 (4) ◽  
pp. 695-705 ◽  
Author(s):  
Yan SHI ◽  
Masaharu MIZUMOTO ◽  
Naoyoshi YUBAZAKI ◽  
Masayuki OTANI
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Rules ◽  
Neuro Fuzzy ◽  
Fuzzy Learning

GT2-CFC: General Type-2 Collaborative Fuzzy Clustering method

2021 ◽  
Author(s):  
Fariba Salehi ◽  
Mohammad Reza Keyvanpour ◽  
Arash Sharifi
Keyword(s):  
Fuzzy Clustering ◽  
General Type ◽  
Clustering Method ◽  
Fuzzy Clustering Method

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.

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