基于神经网络反辨识的电弧炉电极系统解耦控制

Sixth International Conference on Intelligent Systems Design and Applications Pub Date : 2006-10-16 DOI:10.1109/ISDA.2006.253815

Zhang Shao-de

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引用次数: 5

摘要

将基于最近邻聚类算法的RBF神经网络应用于电弧炉三相电极系统。实现了MIMO逆系统的实时在线解耦，将具有强耦合的MIMO系统转化为单个的伪线性对象。在此基础上，将处理线性系统的方法应用于伪线性系统。仿真和实验结果表明，该策略适用于工程应用

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Decoupling Control for Electrode System in Electric Arc Furnace based on Neural Network Inverse Identification

RBF neural network based on nearest neighbor clustering algorithm is applied for three-phase electrode system in electric arc furnace. Real-time on-line decoupling of MIMO inverse system is realized, and transfers MIMO system with strong coupling into individual pseudo linear plant. On the base of these, the method dealing linear system can be used for the pseudo linear system. The simulation and experiments indicate that this strategy is suitable for engineering

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Sixth International Conference on Intelligent Systems Design and Applications

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