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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2015

01.04.2015 | Original Article

Simultaneous estimation of nonlinear parameters in parabolic partial differential equation using quantum-behaved particle swarm optimization with Gaussian mutation

verfasst von: Na Tian, Zhicheng Ji, Choi-Hong Lai

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2015

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Abstract

In this paper, an improved quantum-behaved particle swarm optimization with Gaussian mutation is proposed to simultaneously estimate nonlinear parameters in a one-dimensional parabolic partial differential equation (PDE). No a priori information about the functional form is available, therefore the problems may be treated as function estimation which is difficult to estimate using traditional gradient-based methods. Measurements on the boundary are used in the least square modelling. Tikhonov regularization technique is used to stabilize the ill-posed problem. The numerical benchmark and experiment results demonstrate the validity and efficiency of the proposed method to solve inverse problems of estimating nonlinear parameters in parabolic PDEs.
Vorheriger Artikel Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses
Nächster Artikel Parameter optimization of multi-pass turning using chaotic PSO

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Metadaten
Titel
Simultaneous estimation of nonlinear parameters in parabolic partial differential equation using quantum-behaved particle swarm optimization with Gaussian mutation
verfasst von
Na Tian
Zhicheng Ji
Choi-Hong Lai
Publikationsdatum
01.04.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2015
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0261-1

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