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首页> 外文期刊>Applied Soft Computing >Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network
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Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network

机译:基于聚类粗糙集的大型离心压缩机再制造叶轮使用寿命的预测

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In order to predict the service life of large centrifugal compressor impeller correctly, the rough set and fuzzy Bandelet neural network are combined to construct the novel prediction model which can give full play to theirs advantages. The attribute reduction algorithm based rough set and clustering method is firstly designed to optimize the inputting variables of fuzzy Bandelet neural network. And then the prediction model based on fuzzy Bandelet neural network is proposed, the Bandelet function is used as the excitation function of hidden layer and is combined with fuzzy theory to improve the prediction effectiveness of the prediction model. The training algorithm of fuzzy Bandelet neural network is designed based on improved genetic algorithm, the improved genetic algorithm introduces the adaptive differential evolution method into the traditional genetic algorithm, which can effectively optimize the parameters of fuzzy Bandelet neural network. Finally, the original 30 input variables of fuzzy Bandelet neural network are reduced to 9 input nodes based on rough set using 500 remanufacturing impellers as research objects. The service life of remanufacturing impeller is predicted based on three prediction models, and simulation results show that the fuzzy Bandelet neural network optimized by improved genetic algorithm has highest prediction precision and efficiency, which can correctly predict the service life of remanufacturing impeller. (C) 2019 Elsevier B.V. All rights reserved.
机译:为了正确预测大型离心式压缩机叶轮的使用寿命,粗糙集和模糊的包箱神经网络组合以构建新的预测模型,可以充分发挥其优势。首先旨在基于粗糙集和聚类方法的基于粗糙集和聚类方法,优化模糊Bandelet神经网络的输入变量。然后提出了一种基于模糊Bandelet神经网络的预测模型,用作隐藏层的激发功能,并与模糊理论组合以提高预测模型的预测效率。改进的遗传算法基于改进的遗传算法设计了模糊的Bandelet神经网络的训练算法,其改进的遗传算法将自适应差分演化方法引入传统遗传算法,这可以有效地优化模糊键入神经网络的参数。最后,基于使用500再制造叶轮作为研究对象,基于粗糙集,基于粗糙集的粗糙集,从而减少到9个输入节点。基于三个预测模型预测再制造叶轮的使用寿命,仿真结果表明,通过改进的遗传算法优化的模糊字节神经网络具有最高的预测精度和效率,可以正确预测再制造叶轮的使用寿命。 (c)2019年Elsevier B.V.保留所有权利。

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