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Soft-sensor modeling of cement raw material blending process based on fuzzy neural networks with particle swarm optimization
Wu XG(吴星刚); Yuan MZ(苑明哲); Yu HB(于海斌)
Department工业信息学研究室
Conference NameInternational Conference on Computational Intelligence and Natural Computing, CINC 2009
Conference DateJune 6-7, 2009
Conference PlaceWuhan, China
Author of SourceIEEE Computer Society
Source PublicationProceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, CINC 2009
PublisherIEEE
Publication PlacePiscataway, NJ, United States
2009
Pages158-161
Indexed ByEI ; CPCI(ISTP)
EI Accession number20094512430513
WOS IDWOS:000274875200040
Contribution Rank1
ISBN978-0-7695-3645-3
KeywordArtificial Intelligence Blending Cements Computer Science Concrete Mixing Conjugate Gradient Method Fuzzy Neural Networks Sensor Networks
Abstract

By combining particle swarm optimization algorithm (PSO) with fuzzy neural networks (FNN), a PSO fuzzy neural networks (PSO-FNN) was proposed, which takes full advantage of the global search ability of particle swarm optimization (PSO) algorithm and the local search ability of conjugate gradient algorithm with constraints. The new method assumed that FNN was used to construct the model of cement raw material blending process, while PSO was employed to optimize parameters of FNN. Experiment results show that the model based on PSO-FNN has higher precision and better performance than the model based on BPNN.

Language英语
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/10385
Collection工业信息学研究室
Corresponding AuthorWu XG(吴星刚)
Affiliation1.Key Laboratory of Industrial Informatics, Graduate School of the Chinese Academy of Sciences, Beijing 100039, China
2.Shenyang Institute of Automation Chinese Academy of Sciences, Liaoning 110016, China
Recommended Citation
GB/T 7714
Wu XG,Yuan MZ,Yu HB. Soft-sensor modeling of cement raw material blending process based on fuzzy neural networks with particle swarm optimization[C]//IEEE Computer Society. Piscataway, NJ, United States:IEEE,2009:158-161.
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