Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Parameter Identification Using Hybrid Neural-Genetic Algorithm in Electro-Hydraulic Servo System

신경망-유전자 알고리즘을 이용한 전기${\cdot}$유압 서보시스템의 파라미터 식별

  • 곽동훈 (부산대학교 대학원 지능기계공학과) ;
  • 정봉호 (부산대학교 대학원 지능기계공학과) ;
  • 이춘태 (부산대학교 대학원 지능기계공학과) ;
  • 이진걸 (부산대학교 기계공학부)
  • Published : 2002.11.01
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Abstract

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist 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 genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

Keywords

References

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