Optimized radial basis function network for the fatigue driving modeling-Reference-Cited by-同舟云学术

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Optimized radial basis function network for the fatigue driving modeling

Published:2023-11-28 Issue: Volume: Page:
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Rubio José de Jesús,Islas Marco Antonio,Garcia Donaldo,Pacheco Jaime,Zacarias Alejandro,Aguilar-Ibañez Carlos

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05775-2.pdf

Reference40 articles.

1. Liu H, Wang Z, Fei W, Li J (2020) H-infinity and l2-l-infinity state estimation for delayed memristive neural networks on finite horizon: the round-robin protocol. Neural Netw 132:121–130

2. Li J, Wang Z, Dong H, Fei W (2020) Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities. Neural Netw 130:143–151

3. Zhou Q, Li Y, Zhao D, Li J, Williams H, Xu H, Yan F (2022) Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and gaussian process regression. Appl Energy 305:117853

4. Li J, Zhou Q, He Y, Shuai B, Li Z, Williams H, Xu H (2019) Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles. Appl Energy 253:113617

5. Abbas S, Javaid N, Almogren A, Gulfam SM, Ahmed A, Radwan A (2021) Securing genetic algorithm enabled sdn routing for blockchain based internet of things. IEEE Access 9:139739–139754

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