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首页> 外文会议>ICMIT 2005: Control Systems and Robotics pt.1 >Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system
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Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

机译：电液伺服系统中基于混合遗传算法的参数辨识研究

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摘要

The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms 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. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.
机译：提出了一种混合神经遗传多模型参数估计算法。该方法可应用于电液伺服系统的结构化系统辨识。该算法由递归增量信用分配（ICRA）神经网络和遗传算法组成。 ICRA神经网络评估模型生成的每个成员，而遗传算法则生成新一代模型。为了评估所提出的方法，设计并制造了电动液压伺服系统。通过实验确定了混合神经遗传多模型参数估计算法。结果，获得诸如最小化总平方误差的动力学特性，诸如参数（质量，阻尼系数，体积模量，弹簧系数）。这项研究的结果可以应用于工业领域的液压系统。

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