Multilayer Perceptron
Hassan Ramchoun, Mohammed Amine Janati Idrissi, Youssef Ghanou, Mohamed Ettaouil · 2017
Multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice in particular the activation function type used for each neuron has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture and weights, for solving the obtained model we use the meta-heuristics and we train the network with a back-propagation algorithm.