An online adjusting RBF neural network for nonlinear system modeling-Reference-Cited by-同舟云学术

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An online adjusting RBF neural network for nonlinear system modeling

Published:2022-04-18 Issue:1 Volume:53 Page:440-453
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Jia Lijie,Li Wenjing,Qiao Junfei^ORCID

Funder

National Natural Science Foundation of China

Beijing Municipal Commission of Education

National Key Research and Development Project

Innovative Research Group Project of the National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-021-03106-7.pdf

Reference37 articles.

1. Li F, Zhou JR, Liu C (2018) Statistical modelling of extreme storms using copulas: A comparison study. Coast Eng 142:52–61

2. Liu HP, Zhu YP, Luo Z (2018) PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems. Front Mech Eng 13:390–400

3. Bencherif A, Chouireb F (2019) A recurrent TSK interval type-2 fuzzy neural networks control with online structure and parameter learning for mobile robot trajectory tracking. Appl Intell 49:3881–3893

4. Li WJ, Chu MH, Qiao JF (2020) A pruning feed-forward small-world neural network based on Katz centrality for nonlinear system modeling. Neural Netw 130:269–285

5. Liu F, Song Q, Jin G (2020) The classification and denoising of image noise based on deep neural networks. Appl Intell 50:2194–2207

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