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 to allow the construction of pragmatic models, successive approximations have to be made to permit computational tractibility. The lowest order corresponds to the (Extended) Kalman filter approach to parameter estimation which has already been applied to neural networks. We illustrate some of the deficiencies of the existing approaches and discuss our preliminary generalisations, by considering the application to nonstationary time series.
|Title of host publication||Proceedings of the 4th IEE International Conference on Artificial Neural Networks|
|Place of Publication||Cambridge|
|Number of pages||6|
|Publication status||Published - 26 Jun 1995|
|Event||Proceedings of the 4th IEE International Conference on Artificial Neural Networks - |
Duration: 26 Jun 1995 → 26 Jun 1995
|Conference||Proceedings of the 4th IEE International Conference on Artificial Neural Networks|
|Period||26/06/95 → 26/06/95|
Bibliographical note©1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
- Bayes methods
- Kalman filters
- feedforward neural nets modelling
- parameter estimation
- time series
- computational tractibility
- model order
- complexity estimation
- neural networks
- nonstationary process modelling
- nonstationary time series
- radial basis function networks
Lowe, D., & McLachlan, A. (1995). Modelling of non-stationary processes using radial basis function networks. In Proceedings of the 4th IEE International Conference on Artificial Neural Networks (pp. 300-305). IEEE.