Multilayer Perceptron: Architecture Optimization and Training

Author
Keywords
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.
Year of Publication
2016
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
4
Issue
Special Issue on Artificial Intelligence Underpinning
Number
1
Number of Pages
26-30
Date Published
09/2016
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/JOURNAL/sites/default/files/files/2016/02/ijimai20164_1_5_pdf_30533.pdf
DOI
10.9781/ijimai.2016.415
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