Comparer des méthodes

	Réseau de neurones à ondelettes	Perceptron multicouche (MLP)
Domaine	Séries temporelles	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	1992	1986
Auteur d'origine	Q. Zhang	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
Type	Non-parametric function approximation	Supervised feedforward neural network
Source fondatrice	Zhang, Q., & Benveniste, A. (1992). Wavelet networks. IEEE Transactions on Neural Networks, 3(6), 889–898.	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Alias	WNN, Wavelet-based neural network, Wavelet networks	MLP, feedforward neural network, fully connected neural network, vanilla neural network
Apparentées	2	4
Résumé	A wavelet neural network (WNN) is a function approximation architecture that uses wavelet functions as activation functions in place of traditional sigmoid or ReLU functions. Introduced by Zhang and Benveniste (1992), WNNs combine the multiscale decomposition properties of wavelets with the learning capabilities of neural networks. The result is a flexible nonparametric model that can capture localized features and multi-resolution patterns efficiently, with fewer parameters and better interpretability than standard deep networks.	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.