Neural Networks for Modelling and Control
Neural Networks for Modelling and Control
A revival of interest in Artificial Neural Networks has taken place during the past decade. Research has focused on the ability of these algorithms to learn from their past experiences, generalising the stored information so that it affects the network's response for similar inputs. Adaptive and learning systems are attractive to the control engineer as they can offer significant advantages for modelling and controlling time-varying, complex plants operating in non-stationary environments. This chapter provides an introduction to the ANNs which are commonly used in modelling and control applications and compares their advantages and disadvantages. The text is broadly split into three sections which separately describe the network's modelling abilities, the learning rules and the model evaluation strategies.
18--55
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
1994
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Brown, M. and Harris, C.J.
(1994)
Neural Networks for Modelling and Control.
Harris, C.J.
(ed.)
Advances in Intelligent Control.
.
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Conference or Workshop Item
(Other)
Abstract
A revival of interest in Artificial Neural Networks has taken place during the past decade. Research has focused on the ability of these algorithms to learn from their past experiences, generalising the stored information so that it affects the network's response for similar inputs. Adaptive and learning systems are attractive to the control engineer as they can offer significant advantages for modelling and controlling time-varying, complex plants operating in non-stationary environments. This chapter provides an introduction to the ANNs which are commonly used in modelling and control applications and compares their advantages and disadvantages. The text is broadly split into three sections which separately describe the network's modelling abilities, the learning rules and the model evaluation strategies.
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Published date: 1994
Additional Information:
Address: London, UK
Venue - Dates:
Advances in Intelligent Control, 1994-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250256
URI: http://eprints.soton.ac.uk/id/eprint/250256
PURE UUID: 4e0e812c-ed11-49b6-a8eb-5028e39e6b93
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Date deposited: 04 May 1999
Last modified: 10 Dec 2021 20:07
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Contributors
Author:
M. Brown
Author:
C.J. Harris
Editor:
C.J. Harris
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