NDLI: Neural networks for control

Content Provider	IEEE Xplore Digital Library
Author	Hagan, M.T. Demuth, H.B.
Copyright Year	1999
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA (Hagan, M.T.)
Abstract	Provides a quick overview of neural networks and explains how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The article also presents several control architectures, such as model reference adaptive control, model predictive control, and internal model control, in which multilayer perceptron neural networks can be used as basic building blocks.
Starting Page	1642
Ending Page	1656
File Size	1171133
Page Count	15
File Format	PDF
ISBN	0780349903
ISSN	07431619
DOI	10.1109/ACC.1999.786109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-06-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Neural networks Multilayer perceptrons Predictive models Multi-layer neural network Control systems Function approximation Backpropagation algorithms Training data Adaptive control Predictive control
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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