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OALib Journal期刊
ISSN: 2333-9721
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基于gmdh神经网络的超超临界机组过热蒸汽温度预测模型及仿真研究
, PP. 102-107
Keywords: 超超临界,1000mw机组,过热蒸汽温度,gmdh神经网络,预测模型
Abstract:
由于超超临界1000mw机组过热蒸汽温度控制对象具有大滞后、非线性、动态参数随工况变化大等特点，使得传统的控制方法难以适应过热蒸汽温度的控制，出现过热蒸汽温度波动大，甚至超温等问题。对此，采用数据处理群集方法（gmdh）神经网络建立了过热蒸汽温度动态预测模型，以预测过热蒸汽温度的变化趋势。仿真结果表明，基于gmdh神经网络的过热蒸汽温度预测效果优于线性神经网络和bp神经网络，具有较好的移植性和实用性。
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