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2010
- ISSN: 2010-0248 (Print)
- Abbreviated Title: Int. J. Innov. Manag. Technol.
- Frequency: Quarterly
- DOI: 10.18178/IJIMT
- Editor-in-Chief: Prof. Jin Wang
- Managing Editor: Ms. Nancy Y. Liu
- Abstracting/ Indexing: Google Scholar, CNKI, Ulrich's Periodicals Directory, Crossref, Electronic Journals Library.
E-mail: editor@ijimt.org
- Article Processing Charge: 500 USD
IJIMT 2013 Vol.4(1): 165-169 ISSN: 2010-0248
DOI: 10.7763/IJIMT.2013.V4.383
DOI: 10.7763/IJIMT.2013.V4.383
Analysis of ANN-Based Modelling Approach for Industrial Systems
Hamid Asgari, XiaoQi Chen, and Raazesh Sainudiin
Abstract—A variety of analytical and experimental methods have been suggested so far for industrial system modelling. However, the need for optimized models for different objectives and applications is still a strong motivation for researchers to continue to work in this field. Artificial Neural Network (ANN) as a black-box approach has been playing a significant role in system identification and modelling of many industrial systems during recent decades. Using ANN for modelling purposes is a controversial issue among researchers in different scientific areas. This paper briefly discusses different arising challenges in using ANN-based models for industrial systems and describes advantages and disadvantages of this approach.
Index Terms—Analysis, modelling, system identification, artificial neural network, industrial systems.
Hamid Asgari, XiaoQi Chen and Raazesh Sainudiin are with the Department of Mechanical Engineering, University of Canterbury (UC), Christchurch, New Zealand (e-mail: hamid.asgari@pg.canterbury.ac.nz or asgari_ha@yahoo.com, xiaoqi.chen@canterbury.ac.nz, r.sainudiin@math.canterbury.ac.nz).
Cite: Hamid Asgari, XiaoQi Chen, and Raazesh Sainudiin, "Analysis of ANN-Based Modelling Approach for Industrial Systems," International Journal of Innovation, Management and Technology vol. 4, no. 1, pp. 1, 65-1692013.