首页|Waterfowl breeding environment humidity prediction based on the SRU-based sequence to sequence model
Waterfowl breeding environment humidity prediction based on the SRU-based sequence to sequence model
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NSTL
Elsevier
? 2022Good humidity control is helpful to prevent the occurrence of waterfowl diseases, so it is necessary to predict and control the humidity in waterfowl houses. Traditional sequence methods such as RNN, LSTM, and GRU face challenges in prediction accuracy and parallel performance for long sequence prediction. In this study, a novel neural network model called SRU–SRU-dense is proposed to predict the waterfowl indoor humidity for the next 6 h. Simple recurrent unit(SRU) has better parallel performance compared with LSTM and GRU, which can effectively reduce the inference time. The proposed SRU–SRU-dense model is a seq2seq model based on SRU, and the experimental results show that this model has faster prediction speed and more accurate shorter prediction accuracy than seq2seq models based on RNN, LSTM, and GRU. In addition, we also compared the performance of two different seq2seq structures, seq2seq-dense and seq2seq-sequence, and the experimental results show that the seq2seq-dense structure has faster prediction speed and better prediction accuracy in the prediction of waterfowl indoor humidity in the next 6 h.
Deep learningSequence to sequenceSRUTime series predictionWaterfowl breeding
NSTL
Elsevier
