Single index fuzzy neural networks using locally weighted polynomial regression

2019 ◽  
Vol 368 ◽  
pp. 82-100 ◽  
Author(s):  
Jer-Guang Hsieh ◽  
Jyh-Horng Jeng ◽  
Yih-Lon Lin ◽  
Ying-Sheng Kuo
Keyword(s):  
Neural Networks ◽  
Polynomial Regression ◽  
Fuzzy Neural Networks ◽  
Single Index ◽  
Fuzzy Neural ◽  
Weighted Polynomial Regression

scholarly journals Fuzzy polynomial regression with fuzzy neural networks

2011 ◽  
Vol 35 (11) ◽  
pp. 5400-5412 ◽  
Author(s):  
M. Mosleh ◽  
M. Otadi ◽  
S. Abbasbandy
Keyword(s):  
Neural Networks ◽  
Polynomial Regression ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
Sign in / Sign up

Export Citation Format

Share Document