Breadcrumb

 
 

Neural network-based controllers for an electrothermal furnace system

Title:

Neural network-based controllers for an electrothermal furnace system

Wei, Wenyu (2001) Neural network-based controllers for an electrothermal furnace system. Masters thesis, Concordia University.

[img]
Preview
PDF
2468Kb

Abstract

Neural network schemes are applied in this thesis to a temperature control system problem. The electrothermal furnace is a very popular instrument for applications in material testing area. In this work feedforward neural networks are trained for both identification and control problems of the electrothermal furnace system. The thesis demonstrates that neural networks can be used effectively for this application problem, which is a highly nonlinear dynamical system. The first emphasis is on the electrothermal furnace model identification and the second emphasis is on the design of neural network based PID and internal model control strategies. Both static and dynamic back-propagation methods are discussed. In the electrothermal furnace models that are introduced, multi-layer feedforward networks are interconnected in novel configurations. A novel technique based on the internal model control for nonlinear systems using neural networks is proposed. The control structure proposed directly incorporates a model of the plant that was identified by a neural network and its inverse as part of the control strategy. The potential utilizations of the proposed methods are illustrated through experimental and numerical simulations of an electrothermal furnace system.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Wei, Wenyu
Pagination:xiv, 101 leaves : ill., charts ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.A.Sc.)
Program:Electrical and Computer Engineering
Date:2001
Thesis Supervisor(s):Khorasani, Khashayar
ID Code:1743
Deposited By:Concordia University Libraries
Deposited On:27 Aug 2009 13:22
Last Modified:08 Dec 2010 10:22
Related URLs:
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Document Downloads

More statistics for this item...

Concordia University - Footer