Modelling of Nonstationary Processes Using Radial Basis Function Networks'
نویسنده
چکیده
This paper reports preliminary progress on a principled approach to modelling nonstationary phenomena using neural networks. We are concerned with both parameter and model order complexity estimation. The basic methodology assumes a Bayesian foundation. However t o allow the construction of pragmatic models, successive approximations have to be made l o permit computational tractibility. The lowest order corresponds t o the (Extended) Kalman filter (1) approach 20 parameter estimation which has already been applied to neural networks (2). We illustrate some of the deficiencies of the existing approaches and discuss our preliminary generalisations, b y considering the application t o nonstationary time series.
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