An improvement of neuro-fuzzy learning algorithm for tuning fuzzy rules

2001 ◽  
Vol 118 (2) ◽  
pp. 339-350 ◽  
Author(s):  
Yan Shi ◽  
Masaharu Mizumoto
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Rules ◽  
Neuro Fuzzy ◽  
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 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

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.

A modified gradient-based neuro-fuzzy learning algorithm and its convergence

2010 ◽  
Vol 180 (9) ◽  
pp. 1630-1642 ◽  
Author(s):  
Wei Wu ◽  
Long Li ◽  
Jie Yang ◽  
Yan Liu
Keyword(s):  
Learning Algorithm ◽  
Neuro Fuzzy ◽  
Gradient Based ◽  
Fuzzy Learning

AN IMPROVED INTERNAL MODEL FOR SWARM FORMATION AND ADAPTIVE SWARM BEHAVIOR ACQUISITION

2009 ◽  
Vol 18 (08) ◽  
pp. 1517-1531 ◽  
Author(s):  
TAKASHI KUREMOTO ◽  
YUKI YAMANO ◽  
MASANAO OBAYASHI ◽  
KUNIKAZU KOBAYASHI
Keyword(s):  
Internal Model ◽  
Learning Algorithm ◽  
Learning System ◽  
Neuro Fuzzy ◽  
Swarm Behavior ◽  
Markov Decision ◽  
Behavior Learning ◽  
Improved Learning ◽  
Partially Observable ◽  
Fuzzy Learning

To form a swarm and acquire swarm behaviors adaptive to the environment, we proposed a neuro-fuzzy learning system as a common internal model of each individual recently. The proposed swarm behavior learning system showed its efficient accomplishment in the simulation experiments of goal-exploration problems. However, the input information observed from the environment in our conventional methods was given by coordinate spaces (discrete or continuous) which were difficult to be obtained in the real world by the individuals. This paper intends to improve our previous neuro-fuzzy learning system to deal with the local-limited observation, i.e., usually being a Partially Observable Markov Decision Process (POMDP), by adopting eligibility traces and balancing trade-off between exploration and exploitation to the conventional learning algorithm. Simulations of goal-oriented problems for swarm learning were executed and the results showed the effectiveness of the improved learning system.

Sign in / Sign up

Export Citation Format

Share Document