scholarly journals Automatic Fuzzy Modeling for Ginjo Sake Brewing Process using Fuzzy Neural Networks.

1997 ◽  
Vol 30 (1) ◽  
pp. 94-100 ◽  
Author(s):  
Taizo Hanai ◽  
Akemi Katayama ◽  
Hiroyuki Honda ◽  
Takeshi Kobayashi
Keyword(s):  
Neural Networks ◽  
Fuzzy Modeling ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Brewing Process

Temperature control of Ginjo sake mashing process by automatic fuzzy modeling using fuzzy neural networks

1998 ◽  
Vol 85 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Hiroyuki Honda ◽  
Taizo Hanai ◽  
Akemi Katayama ◽  
Hisao Tohyama ◽  
Takeshi Kobayashi
Keyword(s):  
Neural Networks ◽  
Temperature Control ◽  
Fuzzy Modeling ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

scholarly journals Fuzzy Modeling of Warp Sizing Using Fuzzy Neural Networks.

1994 ◽  
Vol 114 (10) ◽  
pp. 1012-1017
Author(s):  
Seiichi Matsushita ◽  
Akira Kuromiya ◽  
Kyuya Takagi ◽  
Takeshi Furuhashi ◽  
Shin-ichi Horikawa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fuzzy Modeling ◽  
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
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