Adaptive fuzzy iterative learning control based neurostimulation system and in-silico evaluation
Author:
Funder
National Natural Science Foundation of China
Tianjin Municipal Special Program of Talent Development for Excellent Youth Scholars
Publisher
Springer Science and Business Media LLC
Subject
Cognitive Neuroscience
Link
https://link.springer.com/content/pdf/10.1007/s11571-023-10040-6.pdf
Reference51 articles.
1. Ahn H, Chen Y, Moore KL (2007) Iterative learning control: brief survey and categorization. IEEE Trans Syst Man Cyber Part C 37(6):1099–1121. https://doi.org/10.1109/TSMCC.2007.905759
2. Arlotti M, Rosa M, Marceglia S et al (2016) The adaptive deep brain stimulation challenge. Parkinsonism Relat Disord 28:12–17. https://doi.org/10.1016/j.parkreldis.2016.03.020
3. Bronstein JM, Tagliati M, Alterman RL et al (2011) Deep brain stimulation for Parkinson disease. Arch Neurol 68(2):165. https://doi.org/10.1001/archneurol.2010.260
4. Bu X, Hou Z (2018) Adaptive iterative learning control for linear systems with binary-valued observations. IEEE Trans Neural Netw Learn Syst 29(1):232–237. https://doi.org/10.1109/TNNLS.2016.2616885
5. Carron R, Chaillet A, Filipchuk A et al (2013) Closing the loop of deep brain stimulation. Front Syst Neurosci 7:112. https://doi.org/10.3389/fnsys.2013.00112
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