| 基于蝙蝠算法优化模糊神经网络的耙吸挖泥船耙头吸入密度研究 |
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| 引用本文： | 郝光杰,俞孟蕻,苏贞.基于蝙蝠算法优化模糊神经网络的耙吸挖泥船耙头吸入密度研究[J].计算机与数字工程,2022,50(2):436-441. |
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| 作者姓名： | 郝光杰 俞孟蕻 苏贞 |
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| 作者单位： | 江苏科技大学电子信息学院 镇江 212003 |
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| 基金项目： | 中国交通建设股份有限公司科技研发项目 |
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| 摘 要： | 耙吸挖泥船的耙头产量主要取决于耙头的吸入密度,准确的吸入密度预测对提高耙吸挖泥船疏浚产量具有重要的意义。针对目前对吸入密度预测方法存在精度低、实时效果性差的缺点,提出了一种蝙蝠算法与模糊神经网络相结合的预测方法。通过实测施工数据,构建BA-FNN预测模型。实验表明:BA-FNN预测精度高且稳定性能好,能够为耙头产量预测以及指导施工提供科学有效的参考依据。
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| 关 键 词： | 耙吸挖泥船 耙头模型 吸入密度预测 蝙蝠算法(BA) 模糊神经网络(FNN) |
Research on Optimization of Rake Head Density of Suction Hopper Dredger Based on BA and FNN |
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| HAO Guangjie,YU Menghong,SU Zhen.Research on Optimization of Rake Head Density of Suction Hopper Dredger Based on BA and FNN[J].Computer and Digital Engineering,2022,50(2):436-441. |
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| Authors: | HAO Guangjie YU Menghong SU Zhen |
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| Affiliation: | (School of Electronic and Information,Jiangsu University of Science and Technology,Zhenjiang 212003) |
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| Abstract: | The output of drag head of drag suction dredger mainly depends on the suction density of drag head. Accurate prediction of suction density is of great significance to improve the dredging output of drag suction dredger. In view of the shortcomings of low accuracy and poor real-time effect of current prediction methods for inhalation density,a prediction method combining bat algorithm and fuzzy neural network is proposed. Based on the measured construction data,the BA-FNN rake head prediction model is constructed. The results show that BA-FNN has high prediction accuracy and good stability,which can provide scientific and effective reference for production prediction and construction guidance. |
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| Keywords: | hopper dredger rake head model inhalation density prediction bat algorithms fuzzy neural network |
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