Time Delay Prediction of NCS using Tree Structures of Fuzzy Neural Networks

2017 ◽  
Author(s):  
Chang-Wook Han
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fuzzy Neural Networks ◽  
Tree Structures ◽  
Fuzzy Neural ◽  
Delay Prediction

scholarly journals Sistemas neurais híbridos para reconhecimento de padrões em narizes artificiais

2005 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Cleber Zanchettin ◽  
Teresa Bernarda Ludermir
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Delay ◽  
Fuzzy Neural Networks ◽  
Multi Layer Perceptron ◽  
Fuzzy Neural

Este trabalho investiga a utilização de Sistemas Híbridos Inteligentes no sistema de reconhecimento de padrões de um nariz artificial. São abordadas as arquiteturas conexionistas Multi-Layer Perceptron e Time Delay Neural Network; e as arquiteturas híbridas Feature-weighted Detector e Evolving Fuzzy Neural Networks. Além dos classificadores, um filtro Wavelet é avaliado como método de pré-processamento para os sinais de odores. Foram analisados sinais gerados por um nariz artificial, composto por um conjunto de sensores de polímeros condutores, exposto a duas bases de odores distintas.

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.

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