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Python基于RNN的溫室溫度預測研究+源代碼

時間:2019-04-27 15:27來源:畢業論文
根據LSTM的定義,使用Python和基于Python的開源的數值計算擴展NumPy實現LSTM的網絡結構,接著對輸入數據進行處理,最后將預測結果誤差控制在較小范圍內

摘要:能否對溫室溫度進行合理預測是衡量溫室智能化控制程度的一個重要依據。本設計將溫室里濕度、室外溫度、光照強度等因素作為神經網絡的輸入數據,對神經網絡進行訓練,從而對下一時刻的溫室溫度進行預測。由于傳統的時間序列預測分析具有很多問題,使得人們難以快速、方便的建立合適的模型,而適合處理序列數據的RNN則可以解決這個問題。除此之外,RNN的一個模型,LSTM,能夠解決RNN的容易出現的梯度消失、梯度爆炸的問題,所以本設計選擇LSTM進行溫室溫度預測。首先,根據LSTM的定義,使用Python和基于Python的開源的數值計算擴展NumPy實現LSTM的網絡結構,接著對輸入數據進行處理,最后將預測結果誤差控制在較小范圍內。在實驗中,不同的權值初始化、數據的處理方式對結果影響較大和學習率對預測結果影響比較明顯。34941
畢業論文關鍵詞:RNN;長短時記憶網絡 ;溫室 ;NumPy
Research on the Prediction of Temperature in Greenhouse Based on RNN
Abstract: Whether the reasonable forecast of greenhouse temperature is an important basis for measuring the degree of intelligent control of greenhouse. The design uses greenhouse humidity, outdoor temperature, light intensity and other factors as the input data of the neural network to train the neural network, so the next time of the greenhouse temperature is able to be predicted. Because the traditional analysis of time series prediction has many problems, it is difficult to quickly and easily establish the appropriate model. While RNN, suitable for dealing with the serial data, can solve this problem. In addition, the RNN model, LSTM, can solve the problem of RNN prone gradient disappearance and gradient explosion, so this design chooses LSTM to carry out greenhouse temperature prediction. Firstly, according to the definition of LSTM, the NumPy and Python were used to implement the LSTM network structure. The NumPy is a Python-based open source numerical calculation package. Secondly the input data was processed. Finally, the error of prediction was controlled to a small extent. In the experiment, the different value for weights initialized and the different methods for data processing lead to a great influence on the result and the learning rate had obvious influence on the prediction result.
源Z自-六+維L論W文W網^www.aftnzs.live

Keywords: RNN; LSTM; Greenhouse; NumPy
目  錄
摘要:    1
關鍵詞:    1
Abstract    1
Keywords    1
1.    緒論    2
1.1.    研究背景及意義    2
1.2.    國內外研究現狀    2
1.3.    研究內容與技術路線    3
1.3.1.    研究內容    3
1.3.2.    技術路線    3
2.    RNN的介紹和分析    3
2.1    RNN簡介    3
2.2    RNNs的擴展和改進模型    8
2.2.1    Simple RNN(SRN)    8
2.2.2    Bidirectional RNN(雙向網絡)    9
2.2.3    Echo State Network(回聲狀態網絡)    9
3.    長短時記憶網絡(Long Short-Term Memory)    10
3.1.    RNN 和LSTM 的比較    10
3.2.    LSTM 的特點    11
3.3.    LSTM的變體    12
4.    LSTM 實現    13
4.1.    LSTM 前向傳播    14
4.2.    反向傳播    15
4.3.    LSTM 實現    16 Python基于RNN的溫室溫度預測研究+源代碼:http://www.aftnzs.live/jisuanjilunwen/20190427/32672.html
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