NDLI: An air quality forecast model based on the BP neural network of the samples self-organization clustering

Content Provider	IEEE Xplore Digital Library
Author	Jiang Zhifang Mao Bingq Meng Xiangxu Du Xiaoliang Liu Shucheng Li Shenfang
Copyright Year	2010
Description	Author affiliation: Environmental Information Center Shandong Province, Jinan, 250012, China (Mao Bingq; Liu Shucheng) \|\| School of Computer Science and Technology Shandong University, Jinan 250101,China (Jiang Zhifang; Meng Xiangxu; Du Xiaoliang; Li Shenfang)
Abstract	In practice, the training samples of the neural network usually have intrinsic characteristics and regularity. The paper presents a BP neural network (BPNN) forecast model based on the samples self-organizing clustering. Using the clustering feature of the self-organizing competitive neural network(SOCNN), it improves the effect of the training sample to the performance of BPNN. The momentum - adaptive learning rate adjustment algorithm that makes the convergence speed faster with the higher error precision is used for the BPNN in this model. The experiments of the air quality forecast with this model showed that BPNN forecast model based on the samples self-organizing clustering will improve the convergence rate first and reduce the possibility of falling into the local minimum also and improve the forecast accuracy.
Starting Page	1523
Ending Page	1527
File Size	492670
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582643
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Atmospheric modeling Adaptation model Neurons forecast model Artificial neural networks Predictive models Convergence Training BP neural network training samples clustering self-organizing competitive neural network air quality
Content Type	Text
Resource Type	Article

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