Python基于RNN的温室温度预测研究+源代码
毕业论文关键词：RNN；长短时记忆网络 ；温室 ；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.
Keywords: RNN; LSTM; Greenhouse; NumPy
目 录
摘要： 1
关键词： 1
Abstract 1
Keywords 1
1. 绪论 2
1.1. 研究背景及意义 2
1.2. 国内外研究现状 2
1.3. 研究内容与技术路线 3
1.3.1. 研究内容 3
1.3.2. 技术路线 3
2. RNN的介绍和分析 3
2.1 RNN简介 3
2.2 RNNs的扩展和改进模型 8
2.2.1 Simple RNN（SRN） 8
2.2.2 Bidirectional RNN（双向网络） 9
2.2.3 Echo State Network（回声状态网络） 9
3. 长短时记忆网络（Long Short-Term Memory） 10
3.1. RNN 和LSTM 的比较 10
3.2. LSTM 的特点 11
3.3. LSTM的变体 12
4. LSTM 实现 13
4.1. LSTM 前向传播 14
4.2. 反向传播 15
4.3. LSTM 实现 16