Journal article
A novel multicriteria optimization algorithm for the structure determination of multilayer feedforward neural networks
Journal of Network and Computer Applications, Vol.19(2), pp.135-147
1996
Abstract
We propose in this paper a novel prescriptive solution to decide the optimum number of neurons in the hidden-layer of multilayer feedforward neural networks. Our approach uses the unconstrained mixed integer nonlinear multicriteria optimization technique. We validate the algorithm using numerical examples. We extend the above results using fuzzy reasoning and constrained optimization techniques to solve the cross-validation problem in a more effective way than the traditional back propagation algorithm. The main features of our approach are that it is a formal method and it draws results from many fields to combine them for the solution of this NP-complete problem.
Details
- Title
- A novel multicriteria optimization algorithm for the structure determination of multilayer feedforward neural networks
- Authors/Creators
- K. Kottathra (Author/Creator) - BHP Information Technology, Building 3, 151 Kings Street, Warrawong, NSW 2502, AustraliaY. Attikiouzel (Author/Creator) - The University of Western Australia
- Publication Details
- Journal of Network and Computer Applications, Vol.19(2), pp.135-147
- Publisher
- Academic Press Inc
- Identifiers
- 991005542908307891
- Copyright
- © 1996 Academic Press Limited.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.493 Neural-Fuzzy Integration
- Web Of Science research areas
- Computer Science, Hardware & Architecture
- Computer Science, Interdisciplinary Applications
- Computer Science, Software Engineering
- ESI research areas
- Computer Science