Rule Extraction from Fuzzy Neural Networks FuNN: A Method and a Real-World Application

2001 ◽  
Vol 5 (4) ◽  
pp. 193-200 ◽  
Author(s):  
Nikola Kasabov ◽  
◽  
Jaesoo Kim ◽  
Robert Kozma ◽  
Tico Cohen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Rule Extraction ◽  
Fuzzy Neural Networks ◽  
Real Problem ◽  
Levels Of Abstraction ◽  
Real World Application ◽  
General Rules ◽  
Fuzzy Neural ◽  
Different Levels

The paper presents a method for rule extraction from fuzzy neural networks FuNN. The method is applied to a real problem - adaptive control of a wastewater treatment reactor The method extracts from a trained FuNN fuzzy rules that have coefficients of importance and certainty factors attached to their condition and conclusion parts respectively. The rules can have different levels of abstraction depending on the chosen values of several parameters. General rules can be used for explanation and understanding purposes, while precise and specific rules can be used for inference over new data.

Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks

2009 ◽  
Vol 36 (7) ◽  
pp. 10428-10437 ◽  
Author(s):  
Huang Mingzhi ◽  
Wan Jinquan ◽  
Ma Yongwen ◽  
Wang Yan ◽  
Li Weijiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Fuzzy Neural Networks ◽  
Wastewater Treatment Process ◽  
Control Rules ◽  
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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