Multi-input Multi-output Neo-Fuzzy Neural Network for PM10 and PM2.5 Daily Concentrations Forecasting

被引：0

作者：

Terziyska, Margarita ^{[1
]}

Terziyski, Zhelyazko ^{[2
]}

Hadzhikoleva, Stanka ^{[2
]}

Hadzhikolev, Emil ^{[2
]}

机构：

[1] Univ Food Technol, Dept Informat & Stat, Plovdiv, Bulgaria

[2] Univ Plovdiv Paisii Hilendarski, Fac OfMath & Informat, Plovdiv, Bulgaria

来源：

2019 BIG DATA, KNOWLEDGE AND CONTROL SYSTEMS ENGINEERING (BDKCSE) | 2019年

关键词：

neo-fuzzy neuron; MIMO Neo-Fuzzy Network; Hybrid forecasting model; PM2.5 and PM10 forecasting; Air pollution forecasting; Plovdiv;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Multi-Input Multi-Output Neo-Fuzzy Neural Network Structure, based on Neo-fuzzy neuron concept, is used in this study to forecast average daily PM10 and PM2.5 concentration in Plovdiv, Bulgaria. Such a structure is preferred because it has a good generalization capability, high-speed learning, and low computational efforts and guarantee the convergence with the global minimum. The data set used in the present study comprises temperature, humidity, atmospheric pressure, PM10 and PM2.5 daily average concentrations from all 60 monitoring stations located in the city.

引用

页数：7

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