Artificial Neural Network Applications to Feature Recognition

Lian Ding, Yong Yue, Painter John, Mick Walters

Research output: Contribution to conferencePaper

Abstract

This paper presents an overview on current research in feature recognition using artifi-cial neural network (ANN) techniques. ANN-based feature recognition approaches can eliminate some drawbacks of conventional feature recognition methods, and therefore have become an important research focus. The paper discusses the four main issues re-lated to ANN-based feature recognition techniques with the current systems, namely topology of neural network, input representations, training methods and output formats. Finally, it summarises limitations and future research directions.
Original languageEnglish
Pages41-46
Number of pages6
Publication statusPublished - 2000
Event16th National Conference on Manufacturing Research - London
Duration: 5 Sep 20007 Sep 2000

Conference

Conference16th National Conference on Manufacturing Research
CityLondon
Period5/09/007/09/00

Fingerprint

Neural networks
Topology

Cite this

Ding, L., Yue, Y., John, P., & Walters, M. (2000). Artificial Neural Network Applications to Feature Recognition. 41-46. Paper presented at 16th National Conference on Manufacturing Research, London, .

Artificial Neural Network Applications to Feature Recognition. / Ding, Lian; Yue, Yong; John, Painter; Walters, Mick.

2000. 41-46 Paper presented at 16th National Conference on Manufacturing Research, London, .

Research output: Contribution to conferencePaper

Ding, L, Yue, Y, John, P & Walters, M 2000, 'Artificial Neural Network Applications to Feature Recognition' Paper presented at 16th National Conference on Manufacturing Research, London, 5/09/00 - 7/09/00, pp. 41-46.
Ding L, Yue Y, John P, Walters M. Artificial Neural Network Applications to Feature Recognition. 2000. Paper presented at 16th National Conference on Manufacturing Research, London, .
Ding, Lian ; Yue, Yong ; John, Painter ; Walters, Mick. / Artificial Neural Network Applications to Feature Recognition. Paper presented at 16th National Conference on Manufacturing Research, London, .6 p.
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