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Model learning from weights by adaptive enhanced probabilistic convergent network

Lorrentz, P and Howells, WGJ and McDonald-Maier, KD (2010) Model learning from weights by adaptive enhanced probabilistic convergent network. In: UNSPECIFIED, ? - ?.

Model_Learning_from_Weights_by_Adaptive_Enhanced_P.pdf - Accepted Version

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Current weightless classifiers require historical data to model a system and make prediction about a system successfully. Historical data either is not always available or does not take a recent system modification into consideration. For this reason an adaptive filter is designed, which when employed with a weightless classifier enables system model, difficult to characterise system model, and system output prediction, successfully. Results of experiments performed show that the fusion of an adaptive filter and a weightless classifier is more beneficial than the filter or the classifier utilised singly, and that no speed advantage is observed.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings of the 18th European Symposium on Artificial Neural Networks - Computational Intelligence and Machine Learning, ESANN 2010
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 05 Jun 2020 08:16
Last Modified: 05 Jun 2020 08:16

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