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IJIMAI
2016

Multilayer Perceptron: Architecture Optimization and Training

7 years 11 months ago
Multilayer Perceptron: Architecture Optimization and Training
— 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. Keywords — Multilayer Perceptron (MLP), Architecture Optimization, Non-Linear Optimization, GeneticAlgorithm, FeedForward Neural Network Training.
Hassan Ramchoun, Mohammed Amine, Janati Idrissi, Y
Added 05 Apr 2016
Updated 05 Apr 2016
Type Journal
Year 2016
Where IJIMAI
Authors Hassan Ramchoun, Mohammed Amine, Janati Idrissi, Youssef Ghanou, Mohamed Ettaouil
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