ANN model of subdivision error based on genetic algorithm
Qi Ming, ZouJiBin, Shang Jing
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Abstract:
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.
Key words: genetic algorithm artificial neural network (ANN) subdivision error angular measuring system error model
DOI：10.11916/j.issn.1005-9113.2010.01.025
Clc Number:TM93
Fund:
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ANN model of subdivision error based on genetic algorithm
本站小编 哈尔滨工业大学/2019-10-23
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