Author: Shing, Wai-kit
Title: Comparison between two neural networks related controllers
Degree: M.Sc.
Year: 1996
Subject: Neural networks (Computer science)
Automatic control
Back propagation (Artificial intelligence)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: 86 leaves : ill. (some col.) ; 31 cm
Language: English
Abstract: Recently, artificial neural networks have been of interests to the control community. There have been numerous research results showing the potential of neural networks based control systems and some have proven the capabilities of such networks in control. The aim of this dissertation is to implement two different neural network based controllers and compare their performance by using the simulation results. They are direct neural network controller and IMC based neural network controller. Direct neural network controller is used to generate the proper control signal to the plant to achieve the desired performance in the plant output and not necessary to explicitly identify or learn the plant dynamics as conventional neural network controller. In IMC neural network controller, a neural model is used to identify unknown systems and neural controller, which is the inverse of the model, is then trained directly on line. Backpropagation algorithm is used to train the networks and the effect of training parameters to networks is investigated. In the simulation program, the results of the above control systems for a system specified by user can be obtained after inputting the control and system parameters, and then it is compared with those of conventional PID controller, which is designed by Z&N's method.
Rights: All rights reserved
Access: restricted access

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