SPARK 发表于 2025-3-21 16:55:07
书目名称Model Predictive Vibration Control影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0635789<br><br> <br><br>书目名称Model Predictive Vibration Control读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0635789<br><br> <br><br>Dictation 发表于 2025-3-21 20:36:47
https://doi.org/10.1007/978-1-4471-2333-0Active Vibration Damping; Computational Efficiency; Linear Systems; Model Predictive Control; Vibrationconformity 发表于 2025-3-22 00:56:24
Gergely Takács,Boris Rohaľ-IlkivIncludes more than 170 illustrations, photographs, diagrams and several tables to clearly illustrate and explain content.Takes the reader through the necessary steps in understanding the founding idea水槽 发表于 2025-3-22 07:24:35
978-1-4471-6072-4Springer-Verlag London Limited 2012Nibble 发表于 2025-3-22 11:42:58
http://reply.papertrans.cn/64/6358/635789/635789_5.pngabsorbed 发表于 2025-3-22 16:48:43
http://reply.papertrans.cn/64/6358/635789/635789_6.pngCLASP 发表于 2025-3-22 17:50:52
http://reply.papertrans.cn/64/6358/635789/635789_7.png有毒 发表于 2025-3-22 23:47:02
http://reply.papertrans.cn/64/6358/635789/635789_8.png惰性气体 发表于 2025-3-23 01:30:26
MTIRL: Multi-trainer Interactive Reinforcement Learning Systemxperiments show that our aggregation method has the best accuracy when compared with the majority voting, the weighted voting, and the Bayesian method. Finally, we conduct a grid-world experiment to show that the policy trained by the MTIRL with the review model is closer to the optimal policy than that without a review model.额外的事 发表于 2025-3-23 06:20:33
http://reply.papertrans.cn/64/6358/635789/635789_10.png