| 王红君；史丽荣；赵辉；岳有军.基于NARX 神经网络的日光温室湿度预测模型研究[J].广东农业科学,2014,41(20):170-172 |
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基于NARX 神经网络的日光温室湿度预测模型研究 |
| Establishment of sunlight greenhouse humidity prediction model based on NARX neural network |
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| DOI： |
| 中文关键词: 日光温室 湿度 主成分分析 NARX神经网络 |
| 英文关键词: sunlight greenhouse humidity principal component analysis NARX neural network |
| 基金项目:天津市农业科技成果转化与推广计划项目（201203060）；天津市科技计划项目（13TXZYJC43000） |
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| 中文摘要: |
| 在日光温室湿度预测模型建模中，由于输入因子间存在复杂耦合关系以及冗余的条件属性，导致网络训练难以收敛且精度不高。选用影响日光温室湿度的环境因子组成数据样本，采用主成分分析方法对样本集进行解耦降维处理，以采用主成分分析后的数据样本作为输入，以日光温室内湿度作为输出，采用贝叶斯正则化算法构建NARX 神经网络模型，对日光温室湿度进行预测。仿真结果表明，基于NARX 神经网络建立的预测模型具有很强的
非线性动态描述能力，能够对室内湿度值做出准确的预测。 |
| 英文摘要: |
| The neural network training is hard to convergence and the accuracy is not exact, because of the existence
of a complex relationship between the input coupling factor and the condition attribute redundancy in establishing model
for predicting temperature of sunlight greenhouse. To solve the above problems, this article chose the principal component
analysis to treat the samples by dimensionality reduction and decoupling. Using the treated data as input, the humidity of
sunlight greenhouse as output, the model of NARX neural network was established by the Bayesian regularization algorithm
to predict the humidity of sunlight greenhouse. The simulation result showed that the model had strong nonlinear dynamic
description ability, and was able to predict indoor humidity accurately. |
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