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McLachlan, Alan (1997)
Publisher: IEEE
Languages: English
Types: Part of book or chapter of book
Online model order complexity estimation remains one of the key problems in neural network research. The problem is further exacerbated in situations where the underlying system generator is non-stationary. In this paper, we introduce a novelty criterion for resource allocating networks (RANs) which is capable of being applied to both stationary and slowly varying non-stationary problems. The deficiencies of existing novelty criteria are discussed and the relative performances are demonstrated on two real-world problems : electricity load forecasting and exchange rate prediction.
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    • [4] David Lowe and Alan McLachlan. Modelling of nonstationary processes using radial basis function networks. In Fourth I E E International Conference on Artif i c i a l Neural Networks, pages 300-305. IEE Conference Proceedings N o . 409, 1995.
    • [5] Alan McLachlan and David Lowe. Tracking of nonstationary time series using resource allocating R B F networks. In R Trappl, editor, Cybernetics and Syst e m s '96: pages 1066-1071. Austrian Society for Cybernetic Studies, 1996.
    • [6] Ian T Nabney, Alan McLachlan, a n d David Lowe. Practical methods of tracking nonstationary time series applied t o real world d a t a (invited talk). In S K Rogers and D W Ruck, editors, AeroSense '96 - Applications and Science of ilrtijicial Neural Networks 11, pages 152-163. SPIE Publications Vol. 2760, 1996.
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