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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:Qi Ming,ZouJiBin,Shang Jing.ANN model of subdivision error based on genetic algorithm[J].Journal of Harbin Institute Of Technology(New Series),2010,17(1):131-136.DOI:10.11916/j.issn.1005-9113.2010.01.025.
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ANN model of subdivision error based on genetic algorithm
Author NameAffiliation
Qi Ming School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China 
ZouJiBin School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China 
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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