System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Applying Family Competition to Evolution Strategies for Constrained Optimization
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Brief paper: Bayesian estimation via sequential Monte Carlo sampling-Constrained dynamic systems
Automatica (Journal of IFAC)
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Expert Systems with Applications: An International Journal
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Cellular neural networks for gold immunochromatographic strip image segmentation
HIS'12 Proceedings of the First international conference on Health Information Science
A novel switching local evolutionary PSO for quantitative analysis of lateral flow immunoassay
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
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.