Dynamic neural networks with hybrid structures for nonlinear system identification

Deng, Jiamei (2013) Dynamic neural networks with hybrid structures for nonlinear system identification. Engineering Applications of Artificial Intelligence, 26(1), pp. 281-292. ISSN (print) 0952-1976

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Additional Information: NOTICE: the attached file is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence [VOL 26, ISSUE 1, (January 2013)] http://dx.doi.org/10.1016/j.engappai.2012.05.003
Uncontrolled Keywords: recurrent neural networks, system identification, nonlinear systems
Research Area: Applied mathematics
Electrical and electronic engineering
Mechanical, aeronautical and manufacturing engineering
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017) > School of Mechanical and Automotive Engineering
Depositing User: Jiamei Deng
Date Deposited: 09 Jan 2013 11:49
Last Modified: 01 Apr 2014 14:40
DOI: https://doi.org/10.1016/j.engappai.2012.05.003
URI: http://eprints.kingston.ac.uk/id/eprint/23913

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