Multilayer Perceptron: Architecture Optimization and Training

By H. Ramchoun, M. J. Idrissi, Y. Ghanou, M. Ettaouil. Int. J. Interact. Multim. Artif. Intell., 2016

564 citations recorded by the source corpus.

— 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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/404179608460b02dd43baf308e00acd454b81d79

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In Artificial Intelligence

Citations in the corpus

Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, background): “In this article, we will assume that the reader has a working understanding of what a basic artificial neural network is (for an introduction, see Ramchoun, Idrissi, Ghanou, & Ettaouil, 2016; Da Silva, Spatti, Flauzino, Liboni, & dos Reis Alves, 2017).”
Detection of Depression-Related Posts in Reddit Social Media Forum (2019, background): “Multilayer Perceptron (MLP) is a special case of the artificial neural network often used for modelling complex relationships between the input and output layers [58].”