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Combining diverse neural nets

Sharkey, A.J.C. and Sharkey, N.E. (1997) Combining diverse neural nets. The Knowledge Engineering Review, 12 (3). pp. 231-247. ISSN 0269-8889

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Abstract

An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain. However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their reliability. Their performance is typically assessed in terms of their ability to generalise to a previously unseen test set, although unless the training set is very carefully chosen, 100% accuracy is rarely achieved. Improved performance can result when sets of neural nets are combined in ensembles and ensembles can be viewed as an example of the reliability through redundancy approach that is recommended for conventional software and hardware in safety-critical or safety-related applications. Although there has been recent interest in the use of neural net ensembles, such techniques have yet to be applied to the tasks of condition monitoring and fault diagnosis. In this paper, we focus on the benefits of techniques which promote diversity amongst the members of an ensemble, such that there is a minimum number of coincident failures. The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented. This hierarchy is then used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine.

Item Type: Article
Copyright, Publisher and Additional Information: © 1997 Cambridge University Press. Reproduced in accordance with the publisher's self-archiving policy.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Repository Assistant
Date Deposited: 03 Oct 2006
Last Modified: 04 Jun 2014 07:48
Published Version: http://dx.doi.org/10.1017/S0269888997003123
Status: Published
Publisher: Cambridge University Press
Refereed: Yes
Identification Number: 10.1017/S0269888997003123
URI: http://eprints.whiterose.ac.uk/id/eprint/1630

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