Title :
The method of parameter configuration based upon RBF in Self-Organizing Configuration Design for vehicle leaf-spring
Author_Institution :
Sch. of Mech. & Electron. Eng., Huangshi Inst. of Technol., Huangshi, China
Abstract :
The method of Self-Organizing Configuration Design (SOCD) for vehicle leaf-spring can lessen the complexity of process and improve the accuracy of calculation. In order to realize a rapid parameter solving in SOCD for vehicle leaf-spring, a method of parameter configuration is put forward based upon RBF (Radial Basis Function) Neural Network. Starting with problem description of parameter configuration, the feasibility of RBF used for parameter configuration is analyzed and the possible structure of RBF is described. Taking uniform cross section leaf-spring as an example, parameter configuration model based on RBF Neural Network is built by the train of collected samples and the final simulation results show that the model can achieve a comparatively accurate solving of parameter configuration to testify the method in the paper.
Keywords :
automotive components; production engineering computing; radial basis function networks; springs (mechanical); RBF neural network; parameter configuration; radial basis function; self-organizing configuration design; vehicle leaf-spring; Accuracy; Axles; Presses; Simulation; Springs; Training; Vehicles; RBF; example sample; leaf-spring; parameter configuration; product self-organizing configuration design;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022199
