ANN model of subdivision error based on genetic algorithm  

作　　者：齐明 邹继斌 尚静 

机构地区：[1]School of Electrical Engineering and Automation,Harbin Institute of Technology

出　　处：《Journal of Harbin Institute of Technology(New Series)》2010年第1期131-136,共6页哈尔滨工业大学学报（英文版）

摘　　要：According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating

关 键 词：genetic algorithm artificial neural network （ANN） subdivision error angular measuring system error model 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN966[自动化与计算机技术—控制科学与工程]