摘要
溶解氧是水体质量的一个重要参数,合适的溶解氧浓度有利于水产品的生长发育,预测溶解氧变化对水产养殖环境预警有重要的意义。以准确快速预测溶解氧的变化为目标,设计了一种基于优化回声状态网络(ESN)的水产养殖中溶解氧的预测模型。将双向构造训练样本的方法和ESN模型相集成以构建溶解氧预测模型,解决了传统ESN模型存在的网络自由参数定值的问题,以及在储备池规模较大情况下模型泛化性能恶化的问题,很好地解决了在水产养殖中无法快速且准确地预测溶解氧变化的问题。测试对比结果表明,优化后的ESN模型具有良好的鲁棒性;同时在储备池较大规模的情况下,能有效减弱传统ESN出现的过拟合现象,增强了模型的泛化性能。
Dissolved oxygen(DO)is an important parameter for water quality.Appropriate DO concentration range is helpful to the growth of aquatic products.Prediction the concentration change of DO is important for environment early warning.In order to predict DO concentration change accurately and quickly,a prediction model for DO concentration in aquaculture is proposed based on optimized echo state networks(ESN)in this work.The method of bidirectional construction of training samples is integrated with the ESN model to build a DO prediction model,which solves the problem of network free parameter determination in traditional ESN models and performance deterioration when the reserve pool size is large.It also solves the problem that DO cannot be predicted quickly and accurately in aquaculture.The test results show that the improved ESN has good robustness.At the same time,when the reserve pool is relatively large,the overfitting phenomenon of traditional ESN can be effectively reduced,and the generalization performance of the model is improved.
作者
李无言
王志强
蒋永年
郭亚
LI Wu-yan;WANG Zhi-qiang;JIANG Yong-nian;GUO Ya(Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China;Jiangsu Zhongnong Internet of Things Technology Co.,Ltd.,Yixing 214200,China)
出处
《控制工程》
CSCD
北大核心
2023年第3期520-528,共9页
Control Engineering of China
基金
国家自然科学基金资助项目(51961125102,31771680)。
关键词
水产养殖
溶解氧
预测建模
回声状态网络
双向构造
Aquaculture
dissolved oxygen
prediction modeling
echo state networks
bidirectional construction
