OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
McLachlan, Alan
Publisher: IEEE
Languages: English
Types: Part of book or chapter of book
Subjects:
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • John V Candy. Signal Processing : The Model-Based Approach. McGraw-Hill, 1986.
    • Visakan Kadirkamanathan. A statistical inference based growth criterion for the R B F network. In Proceedings of the IEEE Workshop on Neural Networks f o r Sagnal Processing, volume IV, pages 12- 21, 1994.
    • [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.
    • [7] J o h n C P l a t t . A resource allocating network for function interpolation. Neural Computation, 3:213-225, 1991.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok