Fuzzy-Neural Networks Control System Modeling Based on Extension Decision Strategy Generation Mechanism

2008 ◽  
Author(s):  
Luxin Tang ◽  
Zhijun Zhang ◽  
Yunong Zhang ◽  
Hui Liu ◽  
Huan Yao
Keyword(s):  
Neural Networks ◽  
Control System ◽  
System Modeling ◽  
Fuzzy Neural Networks ◽  
Generation Mechanism ◽  
Decision Strategy ◽  
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.

scholarly journals Fault diagnosis of sensors in the control system of a steam turbine

2019 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Mariusz Pawlak ◽  
Janusz Buchta ◽  
Andrzej Oziemski
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Control Systems ◽  
Measurement Data ◽  
Fuzzy Neural Networks ◽  
Sensor Faults ◽  
Fuzzy Neural ◽  
And Control

A diagnostic and control system for a turbine is presented. The influence of the turbine controller on regulation processes in the power system is described. Measured quantities have been characterized and methods for detecting errors have been determined. The paper presents the application of fuzzy neural networks (fuzzy-NNs) for diagnosing sensor faults in the control systems of a steam turbine. The structure of the fuzzy-NN model and the model’s method of learning, based on measurement data, are presented. Fuzzy-NNs are used to detect faults procedures. The fuzzy-NN models are created and verified.

AUTOMATED NONLINEAR SYSTEM MODELING WITH MULTIPLE FUZZY NEURAL NETWORKS AND KERNEL SMOOTHING

2010 ◽  
Vol 20 (05) ◽  
pp. 429-435 ◽  
Author(s):  
WEN YU ◽  
XIAOOU LI
Keyword(s):  
Neural Networks ◽  
Kernel Smoothing ◽  
System Modeling ◽  
Fixed Time ◽  
Fuzzy Neural Networks ◽  
Time Interval ◽  
Nonlinear System Modeling ◽  
Structure Selection ◽  
Fuzzy Neural ◽  
Identification Approach

This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.

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