应用模糊推理系统(FIS)和自适应神经模糊推理系统(ANFIS)技术建模悬浮粒子浓度:以地铁车站为例

IF 1.3 Q4 ENVIRONMENTAL SCIENCES
Zahra Sadat Mousavi Fard, Hassan Asilian Mahabadi, Farahnaz Khajehnasiri, Mohammad Amin Rashidi
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引用次数: 0

摘要

背景:今天,人工智能系统和计算智能的使用正在增加。本研究旨在根据实测数据,确定模糊系统算法来建模和预测地铁站点的空气污染量。方法:本研究首先确定影响地铁站点颗粒物浓度的有效变量。然后测量PM2.5、PM10和总粒径颗粒(TSP)浓度。最后,利用模糊推理系统(FIS)和自适应神经模糊推理系统(ANFIS)对颗粒浓度进行建模。结果:采用模式梯度分割的FIS预测PM2.5、PM10和TSP浓度的准确率为76%,采用聚类和扩散后训练算法(CPDTA)的anfis预测PM2.5、PM10和TSP浓度的准确率为85%。结论:根据研究结果,在本文研究的模型中,ANFIS-FCM-CPDTA由于具有更好的知识提取能力和模糊系统的模糊规则,被认为是一个合适的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the concentration of suspended particles by fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) techniques: A case study in the metro stations
Background: Today, the usage of artificial intelligence systems and computational intelligence is increasing. This study aimed to determine the fuzzy system algorithms to model and predict the amount of air pollution based on the measured data in subway stations. Methods: In this study, first, the effective variables on the concentration of particulate matter were determined in metro stations. Then, PM2.5, PM10, and total size particle (TSP) concentrations were measured. Finally, the particles’ concentration was modeled using fuzzy systems, including the fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS). Results: It was revealed that FIS with modes gradient segmentation (FIS-GS) could predict 76% and ANFIS-FCM with modes of clustering and post-diffusion training algorithm (CPDTA) could predict 85% of PM2.5, PM10, and TSP particle concentrations. Conclusion: According to the results, among the models studied in this work, ANFIS-FCM-CPDTA, due to its better ability to extract knowledge and ambiguous rules of the fuzzy system, was considered a suitable model.
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来源期刊
CiteScore
2.40
自引率
37.50%
发文量
17
审稿时长
12 weeks
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