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Kernel-based regularization least squares algorithm for nonlinear time-delayed systems using self-organizing maps
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DOI：10.1002/rnc.6632.png)
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Abstract
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This article proposes a kernel-based regularization least squares algorithm for nonlinear time-delayed systems with unknown structure. Since the structure and the time-delay of the model are unknown, a model pool constituted of several Volterra series is constructed with the aim of approximating the nonlinear model which has different time-delays. Then, a self-organizing maps method and a kernel-based regularization method are interactively used to update the time-delay and parameters. Compared with the traditional algorithms, the proposed algorithm has no limitation on the nonlinear model, and can describe the dynamics of the nonlinear model with a simple structure. Finally, simulation examples are given to show the effectiveness of the algorithm.
Keywords:
kernel-based regularization method
least squares algorithm
nonlinear time-delayed systems
self-organizing maps
Volterra series
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