[1]周杉杉,李文静,乔俊飞.基于自组织递归模糊神经网络的PM2.5浓度预测[J].智能系统学报,2018,13(4):509-516.[doi:10.11992/tis.201710007]
ZHOU Shanshan,LI Wenjing,QIAO Junfei.Prediction of PM2.5 concentration based on self-organizing recurrent fuzzy neural network[J].CAAI Transactions on Intelligent Systems,2018,13(4):509-516.[doi:10.11992/tis.201710007]
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ZHOU Shanshan,LI Wenjing,QIAO Junfei.Prediction of PM2.5 concentration based on self-organizing recurrent fuzzy neural network[J].CAAI Transactions on Intelligent Systems,2018,13(4):509-516.[doi:10.11992/tis.201710007]
基于自组织递归模糊神经网络的PM2.5浓度预测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第4期
页码:
509-516
栏目:
学术论文—机器学习
出版日期:
2018-07-05
- Title:
- Prediction of PM2.5 concentration based on self-organizing recurrent fuzzy neural network
- Keywords:
- PM2.5; prediction; PCA; recurrent fuzzy neural network; self-organizing; adaptive gradient descent algorithm
- 分类号:
- TP18
- 摘要:
- 针对PM2.5浓度非线性动态变化的特点，提出了一种自组织递归模糊神经网络(self-organizing recurrent fuzzy neural network，SORFNN)方法预测PM2.5小时浓度。首先，通过分析影响PM2.5浓度的多种因素，利用主成分分析法(principal component analysis，PCA)筛选出与PM2.5浓度相关性较强的特征变量作为神经网络的输入变量。然后，根据ε准则和偏最小二乘算法(partial least squares，PLS)进行规则化层神经元的增删，实现递归模糊神经网络结构的自动调整，并采用学习率自适应的梯度下降算法调整模型中心、宽度和权值等参数，建立PM2.5预测模型。最后，利用典型非线性系统辨识和实际PM2.5浓度预测实验进行验证。实验结果表明，所设计的自组织递归模糊神经网络结构精简且预测精度高，较好地满足了PM2.5实时预测的要求。
- Abstract:
- To address the nonlinear dynamic variation in the concentration of fine particulate matter (PM2.5), in this paper, we propose a novel self-organizing recurrent fuzzy neural network (SORFNN) for predicting the hourly PM2.5 concentration. First, we analyzed the factors affecting PM2.5 concentration by principal component analysis to identify the characteristic variables and used them as input variables in the neural network. Next, we added or deleted a nerve cell to the regularized layer, based on the ε criterion and partial least squares algorithm, to automatically adjust the recurrent fuzzy neural network. In addition, we applied the adaptive gradient descent algorithm to adjust parameters such as the centers, widths and weights to establish a PM2.5 model. Lastly, to verify the results, we conducted experiments in typical nonlinear system identification and actual PM2.5 concentration prediction. The experimental results show that the proposed SORFNN is compact in structure, has high prediction accuracy, and can satisfy the real-time prediction requirements of PM2.5 concentration.
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备注/Memo
收稿日期:2017-10-17。
基金项目:国家自然科学基金项目（61533002，61603009）；北京工业大学“日新人才”计划项目（2017-RX（1）-04）；北京市自然科学基金项目（4182007）.
作者简介:周杉杉,女,1992年生,硕士研究生,主要研究方向为智能信息处理与神经网络非线性系统建模。获得软件著作权1项。发表学术论文1篇;李文静,女,1985年生,副教授,博士,主要研究方向为神经计算、人工神经网络、模式识别。主持国家自然科学基金青年项目、中国博士后第57批面上资助项目、北京市博士后科研活动经费资助项目各1项。申请美国发明专利1项。近5年来发表学术论文10余篇,其中SCI收录8篇;乔俊飞,男,1968年生,教授,博士生导师,中国人工智能学会科普工作委员会主任,主要研究方向为智能信息处理、智能控制理论与应用。获教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项。获得发明专利20余项。发表学术论文100余篇,其中被SCI收录20余篇,EI收录60余篇。
通讯作者:周杉杉.E-mail:18810337855@163.com.
更新日期/Last Update:
2018-08-25