A NEURAL-NETWORK MODEL FOR ARM TRAJECTORY FORMATION USING FORWARD AND INVERSE DYNAMICS MODELS
A NEURAL-NETWORK MODEL FOR ARM TRAJECTORY FORMATION USING FORWARD AND INVERSE DYNAMICS MODELS
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DOI：
10.1016/s0893-6080(09)80003-8
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发表时间：
1993-01-01
期刊：
影响因子：
7.8
通讯作者：
KAWATO, M
中科院分区：
文献类型：
--
作者：
WADA, Y;KAWATO, M
最小扭矩变化模型预测和再现人类多关节运动数据相当好。然而，目前基于最小扭矩变化标准的轨迹形成神经网络模型有三个批评：（1）它们的时间空间表示，（2）反向传播是必不可少的，（3）它们需要太多的迭代。因此，我们提出了一种新的基于最小扭矩变化标准的轨迹形成神经网络模型。我们的神经网络模型基本上使用正向动力学模型，反向动力学模型和轨迹形成机制，它生成近似的最小扭矩变化轨迹，它不需要时间的空间表示或反向传播。此外，需要较少的迭代来获得近似最优解。最后，我们的神经网络模型可以广泛应用于工程领域，因为它是一种新的方法来解决优化问题的边界条件。
The minimum torque-change model predicts and reproduces human multi-joint movement data quite well. However, there are three criticisms of the current neural network models for trajectory formation based on the minimum torque-change criteria: (1) their spatial representation of time, (2) back propagation is essential, and (3) they require too many iterations. Accordingly, we propose a new neural network model for trajectory formation based on the minimum torque-change criterion. Our neural network model basically uses a forward dynamics model, an inverse dynamics model, and a trajectory formation mechanism, which generates an approximate minimum torque-change trajectory It does not require spatial representation of time or back propagation. Furthermore, there are less iterations required to obtain an approximate optimal solution. Finally, our neural network model can be broadly applied to the engineering field because it is a new method for solving optimization problems with boundary conditions.