A new model selection strategy in artificial neural networks

Eǧrioǧlu, Erol and Aladaǧ, Çaǧdaş Hakan and Günay, Süleyman (2008) A new model selection strategy in artificial neural networks. Applied Mathematics and Computation, 195 (2). pp. 591-597. ISSN 0096-3003

Full text not available from this repository.

Abstract

In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a new model selection strategy based on well-known model selection criteria is proposed. Proposed strategy is applied to real and simulated time series. Moreover, a new direction accuracy criterion called modified direction accuracy criterion is discussed. The new model selection strategy is more reliable than known model selection criteria.

Item Type: Journal Article
Journal or Publication Title: Applied Mathematics and Computation
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
Departments: Lancaster University Management School > Management Science
Faculty of Science and Technology > Mathematics and Statistics
ID Code: 139571
Deposited By: ep_importer_pure
Deposited On: 13 Dec 2019 15:15
Refereed?: Yes
Published?: Published
Last Modified: 01 Jan 2020 12:22
URI: https://eprints.lancs.ac.uk/id/eprint/139571

Actions (login required)

View Item View Item