基于三维卷积和CLSTM神经网络的水产养殖溶解氧预测

提出了一种基于三维卷积和卷积长短期记忆(convolutional long short-term mem-ory,CLSTM)神经网络的水产养殖溶解氧预测模型.首先,将输入向量及其转置相乘形成一个单通道矩阵,把一定时间段内的单通道矩阵堆叠成一个立方体作为输入数据;然后,将输入数据进行连续两次三维卷积来细化溶解氧相关因...

Prediction of Dissolved Oxygen in Aquaculture Based on 3D Convolution and CLSTM Neural Network

In this paper, a neural network based on three-dimensional (3D) convolution and convolutional long short-term memory (CLSTM) is proposed to predict the dissolved oxygen in aquaculture. Firstly, an input vector is multiplied by its transpose to form a single-channel matrix, and the single-channel matrices within a certain period of time are stacked to form a cube as the input data. Secondly, two consecutive three-dimensional convolutions are carried out on the input data to refine the characteristics of dissolved oxygen related factors, and the pooling layer is deleted for reducing calculation. Finally,the feature results of 3D convolution extraction are sent to the CLSTM model for further information extraction of time dimension, and the data is updated reversely by the gradient descent algorithm through the full connection layer. The actual data of a special aquaculture company in Xiangyang, Hubei Province were collected for experiment, and experimental results show that the proposed model has faster training convergence speed, higher prediction accuracy and better prediction stability than traditional BP neural network models, Conv3D and Conv2D, and could meet the needs of actual production.