Irregular convolution strategy based tensorized type-2 single layer feedforward network-Reference-Cited by-同舟云学术

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Irregular convolution strategy based tensorized type-2 single layer feedforward network

Published:2023-04-27 Issue:9 Volume:14 Page:3129-3159
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Li Jie,Zhao Guoliang,Huang Sharina,Weng Zhi

Funder

National Natural Science Foundation of China

Natural Science Foundation of Inner Mongolia

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01825-6.pdf

Reference54 articles.

1. Huang BH, Ma CF (2021) The iterative solution of a class of tensor equations via Einstein product with a tensor inequality constraint. Linear Multilinear Algebra. 70:6321–6344. https://doi.org/10.1080/03081087.2021.1954140

2. Husmeier D (1999) Random vector functional link (RVFL) networks. Neural networks for conditional probability estimation. Perspectives in neural computing. Springer, London, pp 87–97. https://doi.org/10.1007/978-1-4471-0847-4_6

3. Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No.04CH37541), vol 2, pp 985–990. https://doi.org/10.1109/IJCNN.2004.1380068

4. Huang SRN, Zhao GL, Chen MH (2019) Tensor extreme learning design via generalized Moore-Penrose inverse and triangular type-2 fuzzy sets. Neural Comput Appl 31(9):5641–5651. https://doi.org/10.1007/s00521-018-3385-5

5. Zhao GL, Wu W (2019) Tensor-based type-2 random vector functional link network. In: 2019 Chinese control and decision conference (CCDC), pp 3278–3285. https://doi.org/10.1109/CCDC.2019.8833161

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