NDLI: A Rough Set Based PSO-BPNN Model for Air Pollution Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Zhilong Wang Zengtai Gong Wenjin Zhu Weigang Zhao
Copyright Year	2009
Abstract	Based on rough set theory, a multilayer back propagation neural network (BPNN) whose parameters will be trained and optimized by particle swarm optimization (PSO) is presented here. Making use of the intelligence of RS in knowledge acquisition aspect, this method carries out a pretreatment on the BPNN data, extracts the regulation from large amount of original data, predigests the nerve basics in neural networks, facilitate the neural networks structure, then employ PSO to the weight parameter and finally improve systematic speed and forecasting accuracy. After data pretreatment and attribute reduction by employing RS theory, the noise data and weak interdependency term are eliminated, so the influences during the initialization, study and training process are avoided, and then the weight parameters of each nerve cell have been optimized through PSO, as a result the accuracy of predictions is developed and proved by the evidence of forecasting with time series from the concentration of air pollutant.
Starting Page	357
Ending Page	361
File Size	967671
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge acquisition forecasting Predictive models Intelligent structures Data mining Particle swarm optimization PSO Multi-layer neural network Intelligent networks BPNN Neural networks Air pollution Set theory rough set
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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