Titlebook: Advances in Neural Networks - ISNN 2004; International Sympos Fu-Liang Yin,Jun Wang,Chengan Guo Conference proceedings 2004 Springer-Verlag

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書目名稱Advances in Neural Networks - ISNN 2004影響因子(影響力)




書目名稱Advances in Neural Networks - ISNN 2004影響因子(影響力)學(xué)科排名




書目名稱Advances in Neural Networks - ISNN 2004網(wǎng)絡(luò)公開度




書目名稱Advances in Neural Networks - ISNN 2004網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Neural Networks - ISNN 2004被引頻次




書目名稱Advances in Neural Networks - ISNN 2004被引頻次學(xué)科排名




書目名稱Advances in Neural Networks - ISNN 2004年度引用




書目名稱Advances in Neural Networks - ISNN 2004年度引用學(xué)科排名




書目名稱Advances in Neural Networks - ISNN 2004讀者反饋




書目名稱Advances in Neural Networks - ISNN 2004讀者反饋學(xué)科排名

craven

978-3-540-22843-1Springer-Verlag Berlin Heidelberg 2004

pessimism

Advances in Neural Networks - ISNN 2004978-3-540-28648-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

得罪

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.

松軟無力

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

粗俗人

破布

Fu-Liang Yin,Jun Wang,Chengan GuoIncludes supplementary material:

corn732

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/149140.jpg

geometrician

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