Abstract Recirculating aquaculture has received more and more attention because of its high efficiency of treatment and recycling of aquaculture wastewater. The content of dissolved oxygen is an important indicator of control in recirculating aquaculture, its content and dynamic changes have great impact on the healthy growth of fish. However, changes of dissolved oxygen content are affected by many factors, and there is an obvious time lag between control regulation and effects of dissolved oxygen. To ensure the aquaculture production safety, it is necessary to predict the dissolved oxygen content in advance. The prediction model based on deep belief network has been proposed in this paper to realize the dissolved oxygen content prediction. A variational mode decomposition (VMD) data processing method has been adopted to evaluate the original data space, it takes the data which has been decomposed by the VMD as the input of deep belief network (DBN) to realize the prediction. The VMD method can effectively separate and denoise the raw data, highlight the relations among data features, and effectively improve the quality of the neural network input. The proposed model can quickly and accurately predict the dissolved oxygen content in time series, and the prediction performance meets the needs of actual production. When compared with bagging, AdaBoost, decision tree and convolutional neural network, the VMD-DBN model produces higher prediction accuracy and stability.

中文翻译：

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基于深度置信网络的循环水养殖系统溶解氧预测研究

摘要 循环水养殖因其对养殖废水的高效处理和循环利用而受到越来越多的关注。溶解氧含量是循环水养殖中控制的重要指标，其含量和动态变化对鱼类的健康生长有很大影响。但溶解氧含量的变化受多种因素的影响，控制调节与溶解氧的作用存在明显的时滞。为保证水产养殖生产安全，需要提前预测溶解氧含量。本文提出了基于深度置信网络的预测模型来实现溶解氧含量的预测。采用变分模式分解（VMD）数据处理方法对原始数据空间进行评估，将经过VMD分解的数据作为深度置信网络（DBN）的输入来实现预测。VMD方法可以有效地对原始数据进行分离和去噪，突出数据特征之间的关系，有效提高神经网络输入的质量。该模型能够快速准确地预测时间序列中的溶解氧含量，预测性能满足实际生产需要。与bagging、AdaBoost、决策树和卷积神经网络相比，VMD-DBN模型具有更高的预测精度和稳定性。将经过VMD分解后的数据作为深度信念网络（DBN）的输入来实现预测。VMD方法可以有效地对原始数据进行分离和去噪，突出数据特征之间的关系，有效提高神经网络输入的质量。该模型能够快速准确地预测时间序列中的溶解氧含量，预测性能满足实际生产需要。与bagging、AdaBoost、决策树和卷积神经网络相比，VMD-DBN模型具有更高的预测精度和稳定性。将经过VMD分解后的数据作为深度信念网络（DBN）的输入来实现预测。VMD方法可以有效地对原始数据进行分离和去噪，突出数据特征之间的关系，有效提高神经网络输入的质量。该模型能够快速准确地预测时间序列中的溶解氧含量，预测性能满足实际生产需要。与bagging、AdaBoost、决策树和卷积神经网络相比，VMD-DBN模型具有更高的预测精度和稳定性。该模型能够快速准确地预测时间序列中的溶解氧含量，预测性能满足实际生产需要。与bagging、AdaBoost、决策树和卷积神经网络相比，VMD-DBN模型具有更高的预测精度和稳定性。该模型能够快速准确地预测时间序列中的溶解氧含量，预测性能满足实际生产需要。与bagging、AdaBoost、决策树和卷积神经网络相比，VMD-DBN模型具有更高的预测精度和稳定性。