本文選題：霧霾　切入點：分形理論　出處：《系統(tǒng)科學(xué)與數(shù)學(xué)》2017年02期

【摘要】：隨著工業(yè)化進程的加劇,霧霾已嚴重影響到人類的日常生活,分析天氣因素進而得出影響霧霾天氣的關(guān)鍵因子尤為重要.預(yù)測霧霾天氣形成的關(guān)鍵因子是一個不斷剔除冗余因素保留關(guān)鍵要素的過程,每一個天氣因素都有兩種狀態(tài),被選中為關(guān)鍵因子與否,文章根據(jù)該特點,從一維細胞自動機入手,提出了一種以二元蟻群算法作為搜索策略,分形理論作為子集評估度量準則的混合方法.因二元蟻群算法前期信息素匱乏需要較長搜索時間,引入二元粒子群算法對其進行優(yōu)化,將粒子經(jīng)過多次迭代之后得到的最優(yōu)位置通過模糊函數(shù)映射成螞蟻所需的信息素,在較短的時間內(nèi)形成一條信息素落差明顯的路徑,縮短算法前期運行時間.最后將所用方法應(yīng)用于北京,廣州和上海三地霧霾天氣關(guān)鍵影響因子的預(yù)測中,并結(jié)合10-交叉驗證和SVM算法對預(yù)測結(jié)果分類準確率進行分析,通過與其它算法進行對比,結(jié)果表明文章算法預(yù)測結(jié)果具有較高可信度,為后期的霧霾治理工作提供了重要的參考依據(jù).
[Abstract]:With the aggravation of industrialization, haze has seriously affected the daily life of human beings. It is very important to analyze the weather factors and find out the key factors that affect the haze weather. The key factor to predict the formation of haze weather is a process in which redundant factors are continuously removed and the key elements are retained. Each weather factor has two states. According to this feature, a binary ant colony algorithm is proposed as a search strategy based on one-dimensional cellular automata, which is selected as the key factor or not. Fractal theory is used as a mixed method for subset evaluation metric. Because the lack of pheromone in the early stage of binary ant colony algorithm requires a long search time, the binary particle swarm optimization algorithm is introduced to optimize it. The optimal position of particles after several iterations is mapped to pheromone needed by ants through fuzzy function, and a path with obvious pheromone drop is formed in a short time. Finally, the method is applied to predict the key weather factors of haze in Beijing, Guangzhou and Shanghai, and the classification accuracy of the forecast results is analyzed by combining 10-cross validation and SVM algorithm. Compared with other algorithms, the results show that the prediction results of this paper have high reliability, which provides an important reference for the later work of haze governance.
【作者單位】： 合肥工業(yè)大學(xué)管理學(xué)院;教育部過程優(yōu)化與智能決策重點實驗室;新加坡南洋理工大學(xué)計算智能中心實驗室計算機工程學(xué)院;
【基金】：國家自然科學(xué)基金(71271071,71301041,71490725);國家自然科學(xué)基金重大培育項目(91546108) 國家云制造主題項目(2015AA042101) 安徽省教育廳自然科學(xué)研究項目(KJ2013Z089)資助課題
【分類號】：TP18;X513
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本文編號：1676299