Nonlinear robot system identification based on neural network models
Nonlinear robot system identification based on neural network models
复制标题
基于神经网络模型的非线性机器人系统辨识
DOI：
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发表时间：
1992
期刊：
影响因子：
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通讯作者：
A. Morris
中科院分区：
文献类型：
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作者：
S. Khemaissia;A. Morris
基于人工神经网络模型的非线性泛函特性，研究了机器人操纵臂系统辨识的新问题。描述了一种链路参数的估计过程，其中使用并行递归预测误差技术进行辨识。该算法能够有效地并行更新网络中各神经元的权值，并且比经典的反向传播算法具有更好的收敛性。整个算法可以分布在并行处理器网络上，以实现令人印象深刻的加速。以斯坦福臂的前三个环节为例，验证了该算法的有效性。
This paper addresses the novel issues related to system identification applied to robot manipulators based on the nonlinear functional properties of artificial neural network models. An estimation procedure for the link parameters is described in which identification is carried out using the parallel recursive prediction error technique. The algorithm enables the weights in each neuron of the network to be updated in an efficient parallel manner and has better convergence than the classical back propagation algorithm. The whole of the algorithm can be distributed over a network of parallel processors to achieve impressive speed-up. An example is given for the first three links of the Stanford arm to demonstrate the effectiveness of this algorithm.