Circuits, Systems, and Signal Processing

, Volume 34, Issue 5, pp 1697–1709 | Cite as

Gradient-Based Parameter Identification Algorithms for Observer Canonical State Space Systems Using State Estimates

  • Xingyun Ma
  • Feng Ding
Short Paper


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.


Numerical algorithm Stochastic gradient Identification  Multi-innovation identification theory Kalman filter State space model 



This work was supported by the National Natural Science Foundation of China (No. 61273194) and the PAPD of Jiangsu Higher Education Institutions.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)Jiangnan UniversityWuxiPeople’s Republic of China
  2. 2.Control Science and Engineering Research CenterJiangnan UniversityWuxiPeople’s Republic of China

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