基于小生境算法的空气质量模糊认知图预测  

Air Quality Fuzzy Cognitive Map Forecasting Based on Niche Genetic Algorithm

作　　者：韩慧健[1,2,3] 刘可鑫 林雪 HAN Huijian;LIU Kexin;LIN Xue(School of Computer Science,Shandong University of Finance and Economics,Jinan 250014,China;Shandong Key Laboratory of Digital Media Technology,Jinan 250014,China;Shandong Information Visualization and Computational Economic Technology Research Center,Jinan 250014,China)

机构地区：[1]山东财经大学计算机科学与技术学院,济南250014 [2]山东省数字媒体技术重点实验室,济南250014 [3]山东省信息可视化与计算机工程技术研究中心,济南250014

出　　处：《计算机科学》2024年第S02期975-980,共6页Computer Science

基　　金：国家社会科学基金(22BSH020)。

摘　　要：工业化使得全球经济取得了突飞猛进的增长,但也使得环境污染愈发严重。大气污染成为世界各国讨论的话题。文中提出一种基于小生境遗传算法的改进形式的空气质量模糊认知图预测方法。利用模糊认知图表示空气污染物以及空气质量指数的关联关系,并应用改进的小生境遗传算法优化模型,使得训练结果更接近全局最优解。文中使用2015-2021年的空气数据训练模型,在2022年的数据集上测试模型。实验结果表明,与传统的遗传算法和BP神经网络相比,所提方法预测精度更高,且泛化性能更好,证明了其有效性。Industrialization has led to the rapid growth of global economy,but it has also made environmental pollution more and more serious.Air pollution has become a worldwide hot topic around the world.In this paper,an air quality fuzzy cognitive map forecasting method based on niche genetic algorithm is proposed.This method indicates the relationship of airpollutants and air quality index by using fuzzy cognitive map,and makes the training target moreclose to the global best solution by using modified niche genetic algorithm.The air quality data from 2015 to 2021 is used to train the model,and the model is tested on the 2022 data.Theresult indicates that compared to the traditional genetic algorithm and BP neural network,theproposed method has higher prediction accuracy and better generalization performance,which proves its effectiveness.

关 键 词：空气质量 模糊认知图 小生境遗传算法 

分 类 号：TP3-05[自动化与计算机技术—计算机科学与技术]