Neural networks based sensorial signal fusion: An application to material identification

Tzafestas, SG; Anthopoulos, Y

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Anthopoulos, Y	en
dc.date.accessioned	2014-03-01T02:41:29Z
dc.date.available	2014-03-01T02:41:29Z
dc.date.issued	1997	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30482
dc.subject	Data Acquisition	en
dc.subject	Dynamic Change	en
dc.subject	Intelligent System	en
dc.subject	Sensor Fusion	en
dc.subject	Neural Network	en
dc.subject.other	Data acquisition	en
dc.subject.other	Learning systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Sensor signal fusion	en
dc.subject.other	Sensor data fusion	en
dc.title	Neural networks based sensorial signal fusion: An application to material identification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDSP.1997.628514	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDSP.1997.628514	en
heal.publicationDate	1997	en
heal.abstract	Data acquisition and learning capabilities are necessary for an intelligent system operating in unstructured, dynamically changing environments. For this purpose, a method for the effective use of multiple sensors must be developed. This paper presents how multisensor fusion can be accomplished by neural networks. It first summarizes the conventional fusion techniques and consequently describes the use of neural networks for sensor fusion as well as their advantages. Finally, an application is presented where a neural network is used to fuse the signals of several sensors, of different type, for material identification purposes.	en
heal.publisher	IEEE, Piscataway, NJ, United States	en
heal.journalName	International Conference on Digital Signal Processing, DSP	en
dc.identifier.doi	10.1109/ICDSP.1997.628514	en
dc.identifier.volume	2	en
dc.identifier.spage	923	en
dc.identifier.epage	926	en