基于SOM-PNN神经网络的城市环境风险预测算法研究
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摘要
人工神经网络(ANN)基于生物神经网络的结构与功能,能够对数据进行分布式存储和并行处理。自组织特征映射模型(SOM)和概率神经网络(PNN)是ANN算法常用的模型。文中基于两种模型各自的特点,将两者串联。SOM将由两层神经元组成的二维拓扑结构用于获取和预测数据。PNN模型转换SOM的输出结果,直接输出模型最终分类结果。基于该模型的算法可以提升运算速度,去除了噪声样本的干扰,极大地提升了模型的精度。目前,京津冀区域的环境已陷入较高风险状态之中。文中以京津冀区域SO_2浓度预测为例,运用SOM-PNN模型得到了城市各要素对SO_2浓度影响机制的可视化输出结果和区域环境的高精度预测,进一步验证了该模型的可行性和有效性。
Based on the structure and function of biological neural network,artificial neural network(ANN) can perform distributed storage and parallel processing of data.Self-organizing feature mapping model(SOM) and probabilistic neural network(PNN) are commonly used models in ANN algorithms.Based on the respective characteristics of two mo-dels,the two were connected in series.SOM uses a two-dimensional topology consisting of two layers of neurons to obtain and predict the data.The PNN model converts the output of the SOM and directly outputs the final classification result of the model.The algorithm based on this model can improve the operation speed and remove the interference of noise samples,which greatly improves the accuracy of the model.At present,the Beijing-Tianjin-Hebei regional environment has fallen into a higher risk state.Taking the regional SO_2 concentration prediction as an example,the SOM-PNN model is used to obtain the visual output of the influence mechanism of urban factors on SO_2 concentration and the high-precision prediction of regional environment,which further verifies the feasibility and effectiveness of the proposed model.
Based on the structure and function of biological neural network,artificial neural network(ANN) can perform distributed storage and parallel processing of data.Self-organizing feature mapping model(SOM) and probabilistic neural network(PNN) are commonly used models in ANN algorithms.Based on the respective characteristics of two mo-dels,the two were connected in series.SOM uses a two-dimensional topology consisting of two layers of neurons to obtain and predict the data.The PNN model converts the output of the SOM and directly outputs the final classification result of the model.The algorithm based on this model can improve the operation speed and remove the interference of noise samples,which greatly improves the accuracy of the model.At present,the Beijing-Tianjin-Hebei regional environment has fallen into a higher risk state.Taking the regional SO_2 concentration prediction as an example,the SOM-PNN model is used to obtain the visual output of the influence mechanism of urban factors on SO_2 concentration and the high-precision prediction of regional environment,which further verifies the feasibility and effectiveness of the proposed model.
引文
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1)http://www.ipe.org.cn/Environment/Main.aspx
[2] FRANK R J,DAVEY N,HUNT S P.Time series prediction and neural networks[J].Journal of Intelligent and Robotic Systems,2001,31(1-3):91-103.
[3] ZHANG G P,QI M.Neural network forecasting for seasonal and trend time series[J].European Journal of Operational Research,2005,160(2):501-514.
[4] 王增辉.人工神经网络在医疗诊断中的应用[J/OL].中国科技信息,2018(19):100-102.
[5] 彭琳,林明.基于NARX神经网络的农产品价格时间序列预测方法研究[J].农机化研究,2013,35(11):18-2.
[6] 邹兰.基于SOM-BP神经网络的盾构机故障诊断方法研究[D].西安:西安理工大学,2018.
[7] 岳军,黄诚,任瑞云.PNN神经网络模型在变压器故障诊断建模中的应用[J].自动化技术与应用,2016,35(10):80-82.
[8] KOHONEN T.The ‘neural’ phonetic typewriter[J].Compu- ter,1988,21(3):11-22.
[9] SPECHT D F.Probabilistic neural networks for classification,mapping,or associative memory[C]//IEEE International Conference on Neural Networks.1988,1(24):525-532.
[10] 程盼龙.基于PCA-PNN的冷热冲击箱制冷系统故障诊断研究[D].广州:广东工业大学,2017.
[11] 马峰,夏绍玮,童欣,等.基于SOM-PNN分类器的体数据概率分类及绘制[J].计算机学报,1998(9):819-824.
1)http://www.ipe.org.cn/Environment/Main.aspx