Aquatic product culture water quality prediction method based on deep learning

The invention discloses an aquatic product culture water quality prediction method based on deep learning. By building a deep learning network having three-layer limited Boltzmann machine (RBM) and a layer of BP neural network, water quality sample data is used for training three limited Boltzmann machines by specific dispersion learning for extracting the deep characteristics of the water quality sample data, deep learning network parameter is optimized through BP, so that training on the deep learning network is complete. The trained deep learning network is used to the current water quality sample data, and the water quality prediction can be obtained on an output layer. The method can obtain the characteristic relevance between different water quality factors, and the water quality prediction accuracy is increased. 本发明公开了种基于深度学习的水产养殖水质预测方法。通过搭建个具有三层受限玻尔兹曼机(RBM)和层BP神经网络的深度学习网络。以水质样本数据利用对比散度学习对三个受限玻尔兹曼机进行训练抽取水质样本数据的深度特征，再通过BP对深度学习网络参数进行优化，从而完成深度学习网络的训练。将训练好的深度学习网络应用到当前水质样本数据，在输出层便可获得对水质的预测。本发明可以获得各类不同水质因子之间