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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6424

Title: Identification of nonlinear lateral flow immunoassay state-space models via particle filter approach
Authors: Zeng, N
Wang, Z
Li, Y
Du, M
Liu, X
Keywords: Extended Kalman filter (EKF)
Lateral flow immunoassay (LFIA)
Parameter estimation
Particle filter
State estimation
Publication Date: 2012
Publisher: IEEE
Citation: IEEE Transactions on Nanotechnology, 11(2): 321 - 327, Mar 2012
Abstract: In this paper, the particle filtering approach is used, together with the kernel smoothing method, to identify the state-space model for the lateral flow immunoassay through available but short time-series measurement. The lateral flow immunoassay model is viewed as a nonlinear dynamic stochastic model consisting of the equations for the biochemical reaction system as well as the measurement output. The renowned extended Kalman filter is chosen as the importance density of the particle filter for the purpose of modeling the nonlinear lateral flow immunoassay. By using the developed particle filter, both the states and parameters of the nonlinear state-space model can be identified simultaneously. The identified model is of fundamental significance for the development of lateral flow immunoassay quantification. It is shown that the proposed particle filtering approach works well for modeling the lateral flow immunoassay.
Description: This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEE
Sponsorship: This work was supported in part by the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant 2009I0016.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6081979
http://bura.brunel.ac.uk/handle/2438/6424
DOI: http://dx.doi.org/10.1109/TNANO.2011.2171193
ISSN: 1536-125X
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers
Publications

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