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Constrained Variable Projection Optimization for Stationary RBF-AR Models
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DOI：10.1109/TSMC.2020.3034644.png)
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Abstract
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Stationarity is fundamental for time-series modeling and prediction. In this article, we focus on the radial basis function network-based autoregressive (RBF-AR) models which have been widely used in practical applications. Compared to previous work, we give a less-restrictive sufficient condition for the asymptotic stationarity of the RBF-AR model. The parameter estimation of the RBF-AR model is converted to the optimization of a variable projection functional with constraints of stationarity to always derive a stationary model. The constrained evolutionary algorithm is used to solve the optimization problem. Numerical results demonstrate the effectiveness of the proposed method.
Keywords:
Biological system modeling
Optimization
Numerical models
Time series analysis
Radial basis function networks
Predictive models
Data models
Constrained evolutionary algorithm (CEA)
prediction
radial basis function network-based autoregressive (RBF-AR) model
stationarity
variable projection
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