Multilayer Perceptron: Architecture Optimization and Training

TitleMultilayer Perceptron: Architecture Optimization and Training
Publication TypeJournal Article
Year of Publication2016
AuthorsRamchoun, H., M. A. J. Idrissi, Y. Ghanou, and M. Ettaouil
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueSpecial Issue on Artificial Intelligence Underpinning
Volume4
Number1
Date Published09/2016
Pagination26-30
Abstract

The multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.

KeywordsArchitecture, Genetic Algorithms, Multiplayer Perceptron, Nonlinear Operation, Optimization
DOI10.9781/ijimai.2016.415
URLhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2016/02/ijimai20164_1_5_pdf_30533.pdf
AttachmentSize
ijimai20164_1_5.pdf1.24 MB