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	Vågnätverk	Multilayer Perceptron (MLP)	Återkommande neuralt nätverk
Ämnesområde	Tidsserieanalys	Djupinlärning	Djupinlärning
Familj	Process / pipeline	Machine learning	Machine learning
Ursprungsår	1992	1986	1986–1990
Upphovsperson	Q. Zhang	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	Rumelhart, D. E.; Elman, J. L.
Typ	Non-parametric function approximation	Supervised feedforward neural network	Sequential neural network
Ursprungskälla	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Alias	WNN, Wavelet-based neural network, Wavelet networks	MLP, feedforward neural network, fully connected neural network, vanilla neural network	RNN, Elman network, Jordan network, simple recurrent network
Närliggande	2	4	3
Sammanfattning	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.