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| ウェーブレットニューラルネットワーク× | リカレントニューラルネットワーク (RNN)× | |
|---|---|---|
| 分野≠ | 時系列解析 | 深層学習 |
| 系統≠ | Process / pipeline | Machine learning |
| 提唱年≠ | 1992 | 1986–1990 |
| 提唱者≠ | Q. Zhang | Rumelhart, D. E.; Elman, J. L. |
| 種類≠ | Non-parametric function approximation | Sequential neural network |
| 原典≠ | Zhang, Q., & Benveniste, A. (1992). Wavelet networks. IEEE Transactions on Neural Networks, 3(6), 889–898. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 別名≠ | WNN, Wavelet-based neural network, Wavelet networks | RNN, Elman network, Jordan network, simple recurrent network |
| 関連≠ | 2 | 3 |
| 概要≠ | 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 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. |
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