ANN model of subdivision error based on genetic algorithm

作者：Qi Ming^*; Zou Ji Bin; Shang Jing

来源：Journal of Harbin Institute of Technology, 2010, 17(1): 131-136.

• Artificial Neural Network
• BP algorithm
• Error data
• Error model
• Inductosyns
• Measuring systems
• Simulation result
• Subdivision errors
• Test data
• Training process
• Training sample

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.

• 出版日期2010