• Computer Science
  • Published in
    Proceedings of the 32nd…
    2013

Parameter identification for nonlinear state-space models of a biological network via linearization and robust state estimation

@article{Xiong2013ParameterIF,
  title={Parameter identification for nonlinear state-space models of a biological network via linearization and robust state estimation},
  author={Jie Xiong and Tong Zhou},
  journal={Proceedings of the 32nd Chinese Control Conference},
  year={2013},
  pages={8235-8240}
}
Developing mathematical models of biological systems and estimating their parameters hold a key to understanding and predicting the dynamic behaviors of biological systems which contain gene regulatory networks, signal transduction pathways, etc. A widely adopted way to model dynamic biological systems is to employ nonlinear state-space models, in which the extended Kalman filter (EKF) is sometimes used for estimating both their states and parameters. However, first-order linearization usually… CONTINUE READING

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