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

Li, Y; Pont, MJ; Parikh, CR; Jones, NB

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.

Citation

Li, Y., Pont, M., Parikh, C., & Jones, N. (1999, July). Comparing the performance of three neural classifiers for use in embedded applications. Presented at Recent Advances in Soft Computing Techniques and Applications, Leicester, UK

• Presentation Conference Type: Other
• Conference Name: Recent Advances in Soft Computing Techniques and Applications
• Conference Location: Leicester, UK
• Start Date: Jul 1, 1999
• End Date: Jul 2, 1999
• Publication Date: Jul 1, 2000
• Deposit Date: Jul 27, 2015
• Series Title: Advances in Soft Computing
• Additional Information: Event Type : Conference