scholarly journals Quaternion Neuro-fuzzy Learning Algorithm for Fuzzy Rule Generation

2013 ◽  
Author(s):  
Ryusuke Hata ◽  
Md. Monirul Islam ◽  
Kazuyuki Murase
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Rule Generation ◽  
Neuro Fuzzy ◽  
Fuzzy Learning

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

Self-Tuning for Fuzzy Rule Generation Based upon Fuzzy Singleton-type Reasoning Method

1999 ◽  
Vol 3 (3) ◽  
pp. 200-206 ◽  
Author(s):  
Yan Shi ◽  
◽  
Masaharu Mizumoto ◽  
Keyword(s):  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Rule Generation ◽  
Nonlinear Functions ◽  
Tuning Parameters ◽  
Tuning Method ◽  
Self Tuning

Using fuzzy singleton-type reasoning method, we propose a self-tuning method for fuzzy rule generation. We give a neurofuzzy learning algorithm for tuning fuzzy rules under fuzzy singleton-type reasoning method, then roughly design initial tuning parameters of fuzzy rules based on a fuzzy clustering algorithm before learning a fuzzy model. This should reduce learning time and fuzzy rules generated by our approach are reasonable and suitable for the identified model. We demonstrate our proposal’s efficiency by identifying nonlinear functions.

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.

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
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