摘要
针对对虾养殖投饲预测中存在的特征因子信息不充分、多因子间复杂时序关系挖掘不足以及模型超参数优化效率低等问题,研究构建了一种基于注意力机制和遗传算法优化的长短期记忆网络模型(GA-LSTM-ATTN)。首先,基于溶氧、水温、对虾体长与数量等核心因子,引入生长速率作为补充特征;其次,结合注意力机制,增强模型对多个因素之间关系以及不同生长阶段摄食规律的学习能力;然后,采用遗传算法在模型测试前对时间步长、隐藏层维度、网络深度、训练迭代次数和批量大小等超参数进行全局优化。结果显示,该模型在养殖数据集上的决定系数(R^(2))为0.8683、均方根误差(RMSE)为0.3703、平均绝对误差(MAE)为0.3311。相比基准LSTM模型,R^(2)提升了7.3%,RMSE降低了15.2%,MAE降低了13.5%。与主流预测模型对比,GA-LSTM-ATTN在预测精度上也有所提高。综上,该模型有助于提高对虾投饲量预测的精度,为实际养殖中精准投喂策略的制定提供了理论参考。
To address the limitations of insufficient feature information of characteristic factors,insufficient mining of complex temporal relationships between multiple factors,and low efficiency of model hyperparameter optimization in shrimp breeding and feeding prediction,a long short-term memory network model based on attention mechanism and genetic algorithm optimization(GA-LSTM-ATTN)was constructed.Firstly,based on the core factors such as dissolved oxygen,water temperature,body length and number of shrimp,the growth rate was introduced as a supplementary feature.Secondly,combined with the attention mechanism,the learning ability of the model to the relationship between multiple factors and the feeding rules at different growth stages was enhanced.Then,the genetic algorithm was used to optimize the hyperparameters such as time step,hidden layer dimension,network depth,number of training iterations and batch size before model testing.The results show that the R^(2)(coefficient of determination)=0.8683,RMSE(root mean square error)=0.3703 and MAE(mean absolute error)=0.3311 on the breeding dataset.Compared with the benchmark LSTM model,the R^(2)is increased by 7.3%,the RMSE is reduced by 15.2%,and the MAE is reduced by 13.5%.Compared with the mainstream prediction models,the prediction accuracy of GA-LSTM-ATTN is also improved.In conclusion,the model can effectively improve the accuracy of shrimp feeding prediction,and can provide technical support for accurate feeding in actual aquaculture.
作者
周涛
赵爽
苗玉彬
ZHOU Tao;ZHAO Shuang;MIAO Yubin(School of Machinery,Shanghai Dianji University,Shangha,201306,China;School of Mechanical and Power Engineering,Shanghai Jiaotong University,Shanghai 200240,China)
出处
《渔业现代化》
北大核心
2025年第6期106-114,共9页
Fishery Modernization
基金
上海市农业科技创新项目“南美白对虾无人化设施养殖技术集成应用[沪农科(I2023006)]”。
