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A Hybrid EKF and Switching PSO Algorithm for Joint State and Parameter Estimation of Lateral Flow Immunoassay Models
March/April 2012 (vol. 9 no. 2)
pp. 321-329
Nianyin Zeng, Fuzhou University, Fuzhou
Zidong Wang, Brunel University, Uxbridge
Yurong Li, Fuzhou University, Fuzhou
Min Du, Fuzhou University, Fuzhou
Xiaohui Liu, Brunel University, Uxbridge
In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.

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Index Terms:
Lateral flow immunoassay, extended Kalman filtering, switching particle swarm optimization, constrained optimization, parameter estimation.
Nianyin Zeng, Zidong Wang, Yurong Li, Min Du, Xiaohui Liu, "A Hybrid EKF and Switching PSO Algorithm for Joint State and Parameter Estimation of Lateral Flow Immunoassay Models," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 2, pp. 321-329, March-April 2012, doi:10.1109/TCBB.2011.140
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