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Parameter Identification of an Electro-Hydraulic Servo System Using an Improved Hybrid Neural-Genetic Multimodel Algorithm  

곽동훈 (부산대학교 지능기계공학과)
정봉호 (부산대학교 대학원 지능기계공학과)
이춘태 (부산대학교 대학원 지능기계공학과)
이진걸 (부산대학교 기계공학부)
Publication Information
Journal of the Korean Society for Precision Engineering / v.20, no.5, 2003 , pp. 196-203 More about this Journal
Abstract
This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.
Keywords
Improved hybrid neural-genetic multimodel parameter estimation algorithm; Electro-hydraulic servo system;
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Times Cited By KSCI : 1  (Citation Analysis)
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