Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

• Published in: IEEE transactions on reliability Vol. 70; no. 3; pp. 887 - 900
• Main Authors: Zhang, Dequan, Zhang, Ning, Ye, Nan, Fang, Jianguang, Han, Xu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Approximation; Clustering; Clustering algorithms; End effectors; Hybrid learning algorithm (HLA); industrial robot; Industrial robots; Machine learning; Monte Carlo simulation; Network reliability; Nonlinear systems; Parameter uncertainty; positioning accuracy; Power system reliability; Precision machining; Production methods; Radial basis function; radial basis function network (RBFN); Radial basis function networks; Reliability; Reliability analysis; Robot kinematics; Service robots
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2020.3001232

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.