柔性机构非线性运动的神经网络辨识模型

Identification Model of Flexible Mechanism Nonlinear Motion Based On RBF Neural Network

研究目的是建立柔性机构非线性运动辨识模型.柔性机构的运动形态为高度非线性,利用径向基函数(RBF)神经网络,将机构驱动力矩和机构非线性运动参数分别作为RBF神经网络的输入和输出,利用达到训练精度要求的RBF神经网络进行柔性机构的非线性运动参数的辨识.计算速度快,精度高,为复杂系统的辨识提供了一种理想的建模方法.

The purpose of this study is to set up identification model of flexible mechanism motion. The motive character of flexible mechanism is nonlinear. Radial Basis Function(RBF) neural network is constructed to identify the motive parameter of flexible mechanism. The driving moment and nonlinear motive parameter are considered as the inputs and outputs (samples) of RBF. Nonlinear motive parameters can be identified by trained RBF. The simulation experiments prove it is a high speed and high fidelity method. This method provides a way of model identification for complex system.