Convergence analysis of the batch gradient-based neuro-fuzzy learning algorithm with smoothing L 1/2 regularization for the first-order Takagi–Sugeno system

2017 ◽  
Vol 319 ◽  
pp. 28-49 ◽  
Author(s):  
Yan Liu ◽  
Dakun Yang
Keyword(s):  
Convergence Analysis ◽  
Learning Algorithm ◽  
First Order ◽  
Neuro Fuzzy ◽  
Gradient Based ◽  
Takagi Sugeno ◽  
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

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

A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications

2007 ◽  
Vol 11 (4) ◽  
pp. 365-372 ◽  
Author(s):  
Cheng-Jian Lin ◽  
◽  
Chi-Yung Lee ◽  
Cheng-Hung Chen ◽  
Keyword(s):  
Membership Function ◽  
Fuzzy Inference System ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Layer Structure ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Multi Level ◽  
Takagi Sugeno

In this paper, a novel neuro-fuzzy inference system with multi-level membership function (NFIS_MMF) for classification applications is proposed. The NFIS_MMF model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the NFIS_MMF model contains multi-level membership functions, which are multilevel activation functions. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), fuzzy entropy, and the backpropagation algorithm, is also proposed to construct the NFIS_MMF model and perform parameter learning. Simulations were conducted to show the performance and applicability of the proposed model.

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