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@article{Garcia-Trevino:2024:10.1109/TNNLS.2022.3174705,
author = {Garcia-Trevino, E and Yang, P and Barria, J},
doi = {10.1109/TNNLS.2022.3174705},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {376--389},
title = {Wavelet probabilistic neural networks},
url = {http://dx.doi.org/10.1109/TNNLS.2022.3174705},
volume = {35},
year = {2024}
}

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TY  - JOUR
AB  - 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.
AU  - Garcia-Trevino,E
AU  - Yang,P
AU  - Barria,J
DO  - 10.1109/TNNLS.2022.3174705
EP  - 389
PY  - 2024///
SN  - 1045-9227
SP  - 376
TI  - Wavelet probabilistic neural networks
T2  - IEEE Transactions on Neural Networks and Learning Systems
UR  - http://dx.doi.org/10.1109/TNNLS.2022.3174705
UR  - http://hdl.handle.net/10044/1/97297
VL  - 35
ER  -

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