Learn from one and predict all: single trajectory learning for time delay systems

Author:

Ji Xunbi A.ORCID,Orosz GáborORCID

Funder

Nemzeti Kutatási Fejlesztési és Innovációs Hivatal

Publisher

Springer Science and Business Media LLC

Reference52 articles.

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2. Brunton, S.L., Kutz, J.N.: Data-driven Science and Engineering. Data-driven Science and Engineering Cambridge University Press (2019)

3. Canziani, A., Paszke, A., Culurciello, E.: An analysis of deep neural network models for practical applications. Preprint at arXiv:1605.07678 (2016)

4. Ji, X.A., Molnár, T.G., Avedisov, S.S., Orosz, G.: Feed-forward neural networks with trainable delay. In: 2nd Conference on Learning for Dynamics and Control, vol. 120, pp. 127–136 PMLR (2020)

5. Wong, S., Jiang, L., Walters, R., Molnár, T.G., Orosz, G., Yu, R.: In: 3rd Conference on Learning for Dynamics and Control, pp. 917–929 PMLR (2021)

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1. Trainable Delays in Time Delay Neural Networks for Learning Delayed Dynamics;IEEE Transactions on Neural Networks and Learning Systems;2024

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