使入伍
发表于 2025-3-21 16:54:57
书目名称Advances in Neural Networks - ISNN 2004影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0149140<br><br> <br><br>书目名称Advances in Neural Networks - ISNN 2004读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0149140<br><br> <br><br>
craven
发表于 2025-3-21 21:13:15
978-3-540-22843-1Springer-Verlag Berlin Heidelberg 2004
pessimism
发表于 2025-3-22 03:10:16
Advances in Neural Networks - ISNN 2004978-3-540-28648-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
得罪
发表于 2025-3-22 07:25:28
Networking Models and Standards,nt trajectories to adjust humanoid robot step length and step time based on the sensor information. Compared with GA, RBFNN use less time to generate new trajectory to deal with sudden obstacles after thorough training. The performance of the proposed method is validated by simulation of a 28 DOF humanoid robot model with ADAMS.
松软无力
发表于 2025-3-22 09:43:44
https://doi.org/10.1007/978-1-84628-645-2attenuate the effect of external distributes and parametric uncertainties of the robotic systems. Then a simulation example of 2-DOF robotic systems is given at last, from the simulation results, we can see the well performance of the designed observer and the estimation errors of the joint velocities are negligible.
anachronistic
发表于 2025-3-22 13:50:38
http://reply.papertrans.cn/15/1492/149140/149140_6.png
粗俗人
发表于 2025-3-22 19:36:46
http://reply.papertrans.cn/15/1492/149140/149140_7.png
破布
发表于 2025-3-22 23:08:37
Fu-Liang Yin,Jun Wang,Chengan GuoIncludes supplementary material:
corn732
发表于 2025-3-23 02:09:26
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/149140.jpg
geometrician
发表于 2025-3-23 06:11:15
Networking Models and Standards,nt trajectories to adjust humanoid robot step length and step time based on the sensor information. Compared with GA, RBFNN use less time to generate new trajectory to deal with sudden obstacles after thorough training. The performance of the proposed method is validated by simulation of a 28 DOF hu