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

Research on Optimization of Rake Head Density of Suction Hopper Dredger Based on BA and FNN

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。

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.