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Comparing the performance of three neural classifiers for use in embedded applications

Li, Yuhua, Pont, M. J., Parikh, C. R. and Jones, N. B. 2000. Comparing the performance of three neural classifiers for use in embedded applications. Presented at: Workshop 99 on Recent Advances in Soft Computing, Leicester, England, 01-02 July 1999. Published in: John, Robert and Birkenhead, Ralph eds. Soft Computing Techniques and Applications. Advances in Soft Computing Physica, pp. 34-29.

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Abstract

In this paper, we provide a detailed empirical comparison of three neural-based classifiers used in embedded applications. The three techniques (multi-layer Perceptrons, radial basis function networks and adaptive fuzzy systems) are compared with one another and with a classical kNN classifier. In this study, we observe that the MLP provides similar levels of performance to the RBFN, AFS land kNN) classifiers while exerting a lower computational load on the processor.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Physica
ISBN: 9783790812572
Last Modified: 03 Aug 2020 11:15
URI: http://orca.cf.ac.uk/id/eprint/129135

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