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Wavelet Probabilistic Neural Networks
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DOI：10.1109/TNNLS.2022.3174705.png)
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摘要
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In this article, a novel wavelet probabilistic neural network (WPNN), which is a generative-learning wavelet neural network that relies on the wavelet-based estimation of class probability densities, is proposed. In this new neural network approach, the number of basis functions employed is independent of the number of data inputs, and in that sense, it overcomes the well-known drawback of traditional probabilistic neural networks (PNNs). Since the parameters of the proposed network are updated at a low and constant computational cost, it is particularly aimed at data stream classification and anomaly detection in off-line settings and online environments where the length of data is assumed to be unconstrained. Both synthetic and real-world datasets are used to assess the proposed WPNN. Significant performance enhancements are attained compared to state-of-the-art algorithms.
Keyword:
Probabilistic logic
Biological neural networks
Estimation
Training
Neural networks
Neurons
Training data
Data stream classification
nonstationary environment
online learning
probabilistic neural network (PNN)
wavelet density estimation
wavelet frames
wavelet probabilistic neural networks (WPNNs)
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
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