Balanced simplicity–accuracy neural network model families for system identification-Reference-Cited by-同舟云学术

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Balanced simplicity–accuracy neural network model families for system identification

Published:2014-09-25 Issue:1 Volume:26 Page:171-186
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Romero Ugalde Hector M.,Carmona Jean-Claude,Reyes-Reyes Juan,Alvarado Victor M.,Corbier Christophe

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/s00521-014-1716-8.pdf

Reference54 articles.

1. Aadaleesan P, Miglan N, Sharma R, Saha P (2008) Nonlinear system identification using Wiener type Laguerre–Wavelet network model. Chem Eng Sci 63(15):3932–3941. doi: 10.1016/j.ces.2008.04.043

2. An SQ, Lu T, Ma Y (2010) Simple adaptive control for siso nonlinear systems using neural network based on genetic algorithm. In: Proceedings of the ninth international conference on machine learning and cybernetics IEEE, Qingdao, China

3. Angelov P (2011) Fuzzily connected multimodel systems evolving autonomously from data streams. IEEE Trans Syst Man Cybern Part B Cybern 41(4):898–910. doi: 10.1109/TSMCB.2010.2098866

4. Bebis G, Georgiopoulos M (1994) Feed-forward neural networks: why network size is so important. IEEE Potentials 13(4):27–31

5. Biao L, Qing-chun L, Zhen-hua J, Sheng-fang N (2009) System identification of locomotive diesel engines with autoregressive neural network. In: ICIEA, IEEE, Xi’an, China. doi: 10.1109/ICIEA.2009.5138836

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