Billings, S.A. and Zhu, Q.M. (1994) Model Variation Tests for Multivariable Nonlinear Models Including Neural Networks. Research Report. ACSE Research Report 484 . Department of Automatic Control and Systems Engineering
Abstract
A fast and concise MIMO nonlinear model validity test procedure is derived, based on higher order correlation functions, to form a global to local hierarchical validation diagnosis of identified MIMO linear and nonlinear models. The new procedure is applied to four MIMO nonlinear system models including a neural network training example to demonstrate the effectiveness of the tests.
Metadata
Item Type: | Monograph |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports |
Depositing User: | MRS ALISON THERESA BARNETT |
Date Deposited: | 24 Jun 2014 11:51 |
Last Modified: | 25 Oct 2016 14:40 |
Status: | Published |
Publisher: | Department of Automatic Control and Systems Engineering |
Series Name: | ACSE Research Report 484 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79500 |