Difference between revisions of "Publications:Using artificial neural networks for process and system modelling"
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| − | |Name=Verikas, Antanas | + | |Name=Verikas, Antanas (av) (0000-0003-2185-8973) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938));Bacauskiene, M. (Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania) |
|Title=Using artificial neural networks for process and system modelling | |Title=Using artificial neural networks for process and system modelling | ||
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Latest revision as of 22:40, 30 September 2016
| Title | Using artificial neural networks for process and system modelling |
|---|---|
| Author | Antanas Verikas and M. Bacauskiene |
| Year | 2003 |
| PublicationType | Journal Paper |
| Journal | Chemometrics and Intelligent Laboratory Systems |
| HostPublication | |
| Conference | |
| DOI | http://dx.doi.org/10.1016/S0169-7439(03)00093-5 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:237392 |
| Abstract | This letter concerns several papers, devoted to neural network-based process and system modelling, recently published in the Chemometrics and Intelligent Laboratory Systems journal. Artificial neural networks have proved themselves to be very useful in various modelling applications, because they can represent complex mapping functions and discover the representations using powerful learning algorithms. An optimal set of parameters for defining the functions is learned from examples by minimizing an error functional. In various practical applications, the number of examples available for estimating parameters of the models is rather limited. Moreover, to discover the best model, numerous candidate models must be trained and evaluated. In such thin-data situations, special precautions are to be taken to avoid erroneous conclusions. In this letter, we discuss three important issues, namely network initialization, over-fitting, and model selection, the right consideration of which can be of tremendous help in successful network design and can make neural modelling results more valuable. |
