Selection and Recognition of Statistically Defined Signals in Learning Systems
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Selection and Recognition of Statistically Defined Signals in Learning Systems. / Bezruk, Valeriy ; Omelchenko, Anatolii ; Fedorov, Oleksii et al.
IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society: Proceedings. Piscataway : IEEE - Institute of Electrical and Electronics Engineers Inc., 2018. S. 3211-3216 8591321 (Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - Selection and Recognition of Statistically Defined Signals in Learning Systems
AU - Bezruk, Valeriy
AU - Omelchenko, Anatolii
AU - Fedorov, Oleksii
AU - Mercorelli, Paolo
AU - Nieto Hipólito, Juan Ivan
PY - 2018/12/26
Y1 - 2018/12/26
N2 - The paper addresses a non-traditional problem of pattern recognition, when information about pattern is represented in the form of a random signal taken from the output of a corresponding physical sensor. It is supposed that there exist two types of signal to recognize, namely, specified in the statistical sense signals and totally unknown signals. Such the conditions are called conditions of increased a priory uncertainty. Developing a technique to recognize specified signals in conditions of increased a priory uncertainty is the objective of this paper. Methods for selection and recognition of a statistically defined random signal are proposed for the cases when signal description is done by various probabilistic models. Additional consideration is given to peculiarities of employing these methods for solving applied problems of pattern recognition in radar, medical diagnostics and speaker identification.
AB - The paper addresses a non-traditional problem of pattern recognition, when information about pattern is represented in the form of a random signal taken from the output of a corresponding physical sensor. It is supposed that there exist two types of signal to recognize, namely, specified in the statistical sense signals and totally unknown signals. Such the conditions are called conditions of increased a priory uncertainty. Developing a technique to recognize specified signals in conditions of increased a priory uncertainty is the objective of this paper. Methods for selection and recognition of a statistically defined random signal are proposed for the cases when signal description is done by various probabilistic models. Additional consideration is given to peculiarities of employing these methods for solving applied problems of pattern recognition in radar, medical diagnostics and speaker identification.
KW - Engineering
KW - radio location
KW - pattern recognition
KW - random signal
KW - probalistic model
KW - decision rule
UR - http://www.scopus.com/inward/record.url?scp=85061549311&partnerID=8YFLogxK
U2 - 10.1109/IECON.2018.8591321
DO - 10.1109/IECON.2018.8591321
M3 - Article in conference proceedings
SN - 978-1-5090-6685-8
T3 - Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
SP - 3211
EP - 3216
BT - IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
ER -