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

Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478, 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:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129135

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