Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method.

Automated summary

This study focuses on the reliability analysis of positioning accuracy for industrial robots, utilizing a radial basis function network to construct a mapping relationship and combining with Monte Carlo simulation method for evaluation. A novel hybrid learning algorithm is proposed for training the network, demonstrating high proficiency and reliability of the method through examples.