黄永刚, 饶运章, 刘剑, 张学焱. 神经网络与遗传算法预测充填配比的研究[J]. 有色金属科学与工程, 2016, 7(5): 76-80. DOI: 10.13264/j.cnki.ysjikx.2016.05.014
引用本文: 黄永刚, 饶运章, 刘剑, 张学焱. 神经网络与遗传算法预测充填配比的研究[J]. 有色金属科学与工程, 2016, 7(5): 76-80. DOI: 10.13264/j.cnki.ysjikx.2016.05.014
HUANG Yonggang, RAO Yunzhang, LIU Jian, ZHANG Xueyan. Study on the prediction of the filling ratio with neural network and genetic algorithm[J]. Nonferrous Metals Science and Engineering, 2016, 7(5): 76-80. DOI: 10.13264/j.cnki.ysjikx.2016.05.014
Citation: HUANG Yonggang, RAO Yunzhang, LIU Jian, ZHANG Xueyan. Study on the prediction of the filling ratio with neural network and genetic algorithm[J]. Nonferrous Metals Science and Engineering, 2016, 7(5): 76-80. DOI: 10.13264/j.cnki.ysjikx.2016.05.014

神经网络与遗传算法预测充填配比的研究

Study on the prediction of the filling ratio with neural network and genetic algorithm

  • 摘要: 为确定最优充填配比方案,在实验的基础上,基于神经网络遗传算法,预测全局最优充填实验条件,最优实验条件为灰砂比0.2024,养护时间5.863 d,溶度67.8 %,最大充填体抗压强度0.6777 MPa,与实际最佳配比方案灰砂比1:4、养护天数28 d、溶度75 %、最大抗压强度5.48 MPa相差较大,预测结果不是很满意。说明该方法有较强的适用条件,神经网络的预测精度对遗传算法的极值寻优有影响,建议扩大样本的数量。

     

    Abstract: In order to determine the optimum scheme of filling ratio, on the basis of experimental, neural network and genetic algorithm based on predicted global optimal filling experimental conditions, optimal experimental conditions sand than 0.2024, curing time day 5.863 and solubility 67.8%, filling the largest compressive strength 0.6777 MPa, and the best ratio of lime sand ratio 1:4, curing days 28 days, solubility of 75%, the maximum compressive strength 5.48 MPa difference is large, the prediction results is not very satisfied. The method has good applicability, and the prediction accuracy of neural network has an effect on the optimization of genetic algorithm.

     

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