caa a22 4500 606206353 CHVBK 20210128101011.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00034-014-9911-5 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9911-5 Gradient-Based Parameter Identification Algorithms for Observer Canonical State Space Systems Using State Estimates [Elektronische Daten] [Xingyun Ma, Feng Ding] This paper considers the parameter identification problem of the state space observer canonical model for linear stochastic systems, and proposes a Kalman filter-based gradient iterative algorithm and an observer-based multi-innovation stochastic gradient algorithm. The fundamental idea is to replace the unmeasurable states in the information vector with the estimated states and to compute the states of the systems through the Kalman filter or the state observer using the previous parameter estimates. Examples are provided to confirm the effectiveness of the proposed algorithms. Springer Science+Business Media New York, 2014 Numerical algorithm nationallicence Stochastic gradient nationallicence Identification nationallicence Multi-innovation identification theory nationallicence Kalman filter nationallicence State space model nationallicence Ma Xingyun Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, 214122, Wuxi, People's Republic of China aut Ding Feng Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, 214122, Wuxi, People's Republic of China aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/5(2015-05-01), 1697-1709 0278-081X 34:5<1697 2015 34 34 https://doi.org/10.1007/s00034-014-9911-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9911-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ma Xingyun Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, 214122, Wuxi, People's Republic of China aut NATIONALLICENCE 700 1- Ding Feng Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, 214122, Wuxi, People's Republic of China aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/5(2015-05-01), 1697-1709 0278-081X 34:5<1697 2015 34 34