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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Li, Yuhua, Pont, MJ, Parikh, CR and Jones, NB (2000) Comparing the performance of three neural classifiers for use in embedded applications. In: SOFT COMPUTING TECHNIQUES AND APPLICATIONS. UNSPECIFIED. 6 pp. [Conference contribution]

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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 contribution (Paper)
Keywords:multi-layer perceptron network; radial basis function network; adaptive fuzzy system; k-nearest neighbour; embedded system
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:5983
Deposited By:Dr Yuhua Li
Deposited On:09 Mar 2010 16:13
Last Modified:09 Mar 2010 16:13

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