Robust Deep Neural Network Using Fuzzy Denoising Autoencoder-Reference-Cited by-同舟云学术

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Robust Deep Neural Network Using Fuzzy Denoising Autoencoder

Published:2020-04-16 Issue:4 Volume:22 Page:1356-1375
ISSN:1562-2479
Container-title:International Journal of Fuzzy Systems
language:en
Short-container-title:Int. J. Fuzzy Syst.

Author:

Han Hong-Gui,Zhang Hui-Juan,Qiao Jun-Fei

Funder

National Key Research and Development Project

National Science Foundation of China

Beijing Natural Science Foundation

Beijing Outstanding Young Scientist Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s40815-020-00845-6.pdf

Reference48 articles.

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2. Hinton, G., Deng, L., Dong, Y.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag. 29(6), 82–97 (2012)

3. Hwang, M., Wang, D., Jiang, W.C., Pan, X.: An adaptive regularization approach to colonoscopic polyp detection using a cascaded structure of encoder–decoders. Int. J. Fuzzy Syst. 21(7), 2091–2101 (2019)

4. Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)

5. Saad, E.W., Prokhorov, D.V., Wunsch, D.C.: Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Trans. on Neural Netw. 9(6), 1456–1470 (1998)

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