Modelling and FDI of dynamic discrete time systems using a MLP with a new sigmoidal activation function

Skoundrianos, EN; Tzafestas, SG

dc.contributor.author	Skoundrianos, EN	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T01:21:04Z
dc.date.available	2014-03-01T01:21:04Z
dc.date.issued	2004	en
dc.identifier.issn	0921-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16056
dc.subject	sigmoidal activation functions	en
dc.subject	system modelling	en
dc.subject	fault detection and identification	en
dc.subject	multi-layer perceptron	en
dc.subject	three tank system	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Robotics	en
dc.subject.other	NEURAL-NETWORKS	en
dc.title	Modelling and FDI of dynamic discrete time systems using a MLP with a new sigmoidal activation function	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/B:JINT.0000049175.78893.2f	en
heal.identifier.secondary	http://dx.doi.org/10.1023/B:JINT.0000049175.78893.2f	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	In this paper we investigate the use of the multi-layer perceptron (MLP) for system modelling. A new sigmoidal activation function is introduced and the study is focused at the utilization of this function on a MLP that performs modelling of dynamic, discrete time systems. The role of the activation function in the training process is investigated analytically, and it is proven that the shape of the activation function and it's derivative can affect the training outcome. The method is simulated at a well known benchmark, namely the three tank system, and is incorporated in a Fault Detection and Identification (FDI) method, also applied and simulated at the three tank system. Finally, a comparison is made with an approach that utilizes local model neural networks for system modeling.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS	en
dc.identifier.doi	10.1023/B:JINT.0000049175.78893.2f	en
dc.identifier.isi	ISI:000225299500002	en
dc.identifier.volume	41	en
dc.identifier.issue	1	en
dc.identifier.spage	19	en
dc.identifier.epage	36	en