Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace

For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten ir...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Junpeng [verfasserIn] Hua, Changchun Qian, Junlei Guan, Xinping

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021transfer abstract

Schlagwörter:	Multi-Input Multi-Output Blast furnace Silicon content Takagi-Sugeno fuzzy model Molten iron quality

Umfang:	15

Übergeordnetes Werk:	Enthalten in: Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species - Polizzi, P. ELSEVIER, 2017transfer abstract, [S.l.]
Übergeordnetes Werk:	volume:421 ; year:2021 ; day:30 ; month:09 ; pages:178-192 ; extent:15

Links:	Volltext

DOI / URN:	10.1016/j.fss.2020.08.012

Katalog-ID:	ELV054927781

Internformat


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520			\|a For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data.
520			\|a For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data.
650		7	\|a Multi-Input Multi-Output \|2 Elsevier
650		7	\|a Blast furnace \|2 Elsevier
650		7	\|a Silicon content \|2 Elsevier
650		7	\|a Takagi-Sugeno fuzzy model \|2 Elsevier
650		7	\|a Molten iron quality \|2 Elsevier
700	1		\|a Hua, Changchun \|4 oth
700	1		\|a Qian, Junlei \|4 oth
700	1		\|a Guan, Xinping \|4 oth
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936	b	k	\|a 44.40 \|j Pharmazie \|j Pharmazeutika \|q VZ
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Indexfelder

author_variant	j l jl
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hierarchy_sort_str	2021transfer abstract
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publishDate	2021
allfields	10.1016/j.fss.2020.08.012 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001486.pica (DE-627)ELV054927781 (ELSEVIER)S0165-0114(20)30318-3 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Li, Junpeng verfasserin aut Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Elsevier Hua, Changchun oth Qian, Junlei oth Guan, Xinping oth Enthalten in Elsevier Polizzi, P. ELSEVIER Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species 2017transfer abstract [S.l.] (DE-627)ELV020637101 volume:421 year:2021 day:30 month:09 pages:178-192 extent:15 https://doi.org/10.1016/j.fss.2020.08.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_50 GBV_ILN_55 GBV_ILN_60 GBV_ILN_61 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_90 GBV_ILN_92 GBV_ILN_104 GBV_ILN_105 GBV_ILN_120 GBV_ILN_121 GBV_ILN_122 GBV_ILN_130 GBV_ILN_131 GBV_ILN_147 GBV_ILN_160 GBV_ILN_179 GBV_ILN_181 GBV_ILN_276 GBV_ILN_737 GBV_ILN_754 GBV_ILN_812 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2023 GBV_ILN_2024 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2032 GBV_ILN_2033 GBV_ILN_2035 GBV_ILN_2040 GBV_ILN_2043 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2065 GBV_ILN_2084 GBV_ILN_2121 GBV_ILN_2227 GBV_ILN_2502 GBV_ILN_2505 GBV_ILN_2508 44.40 Pharmazie Pharmazeutika VZ AR 421 2021 30 0930 178-192 15
spelling	10.1016/j.fss.2020.08.012 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001486.pica (DE-627)ELV054927781 (ELSEVIER)S0165-0114(20)30318-3 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Li, Junpeng verfasserin aut Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Elsevier Hua, Changchun oth Qian, Junlei oth Guan, Xinping oth Enthalten in Elsevier Polizzi, P. ELSEVIER Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species 2017transfer abstract [S.l.] (DE-627)ELV020637101 volume:421 year:2021 day:30 month:09 pages:178-192 extent:15 https://doi.org/10.1016/j.fss.2020.08.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_50 GBV_ILN_55 GBV_ILN_60 GBV_ILN_61 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_90 GBV_ILN_92 GBV_ILN_104 GBV_ILN_105 GBV_ILN_120 GBV_ILN_121 GBV_ILN_122 GBV_ILN_130 GBV_ILN_131 GBV_ILN_147 GBV_ILN_160 GBV_ILN_179 GBV_ILN_181 GBV_ILN_276 GBV_ILN_737 GBV_ILN_754 GBV_ILN_812 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2023 GBV_ILN_2024 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2032 GBV_ILN_2033 GBV_ILN_2035 GBV_ILN_2040 GBV_ILN_2043 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2065 GBV_ILN_2084 GBV_ILN_2121 GBV_ILN_2227 GBV_ILN_2502 GBV_ILN_2505 GBV_ILN_2508 44.40 Pharmazie Pharmazeutika VZ AR 421 2021 30 0930 178-192 15
allfields_unstemmed	10.1016/j.fss.2020.08.012 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001486.pica (DE-627)ELV054927781 (ELSEVIER)S0165-0114(20)30318-3 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Li, Junpeng verfasserin aut Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Elsevier Hua, Changchun oth Qian, Junlei oth Guan, Xinping oth Enthalten in Elsevier Polizzi, P. ELSEVIER Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species 2017transfer abstract [S.l.] (DE-627)ELV020637101 volume:421 year:2021 day:30 month:09 pages:178-192 extent:15 https://doi.org/10.1016/j.fss.2020.08.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_50 GBV_ILN_55 GBV_ILN_60 GBV_ILN_61 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_90 GBV_ILN_92 GBV_ILN_104 GBV_ILN_105 GBV_ILN_120 GBV_ILN_121 GBV_ILN_122 GBV_ILN_130 GBV_ILN_131 GBV_ILN_147 GBV_ILN_160 GBV_ILN_179 GBV_ILN_181 GBV_ILN_276 GBV_ILN_737 GBV_ILN_754 GBV_ILN_812 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2023 GBV_ILN_2024 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2032 GBV_ILN_2033 GBV_ILN_2035 GBV_ILN_2040 GBV_ILN_2043 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2065 GBV_ILN_2084 GBV_ILN_2121 GBV_ILN_2227 GBV_ILN_2502 GBV_ILN_2505 GBV_ILN_2508 44.40 Pharmazie Pharmazeutika VZ AR 421 2021 30 0930 178-192 15
allfieldsGer	10.1016/j.fss.2020.08.012 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001486.pica (DE-627)ELV054927781 (ELSEVIER)S0165-0114(20)30318-3 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Li, Junpeng verfasserin aut Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Elsevier Hua, Changchun oth Qian, Junlei oth Guan, Xinping oth Enthalten in Elsevier Polizzi, P. ELSEVIER Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species 2017transfer abstract [S.l.] (DE-627)ELV020637101 volume:421 year:2021 day:30 month:09 pages:178-192 extent:15 https://doi.org/10.1016/j.fss.2020.08.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_50 GBV_ILN_55 GBV_ILN_60 GBV_ILN_61 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_90 GBV_ILN_92 GBV_ILN_104 GBV_ILN_105 GBV_ILN_120 GBV_ILN_121 GBV_ILN_122 GBV_ILN_130 GBV_ILN_131 GBV_ILN_147 GBV_ILN_160 GBV_ILN_179 GBV_ILN_181 GBV_ILN_276 GBV_ILN_737 GBV_ILN_754 GBV_ILN_812 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2023 GBV_ILN_2024 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2032 GBV_ILN_2033 GBV_ILN_2035 GBV_ILN_2040 GBV_ILN_2043 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2065 GBV_ILN_2084 GBV_ILN_2121 GBV_ILN_2227 GBV_ILN_2502 GBV_ILN_2505 GBV_ILN_2508 44.40 Pharmazie Pharmazeutika VZ AR 421 2021 30 0930 178-192 15
allfieldsSound	10.1016/j.fss.2020.08.012 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001486.pica (DE-627)ELV054927781 (ELSEVIER)S0165-0114(20)30318-3 DE-627 ger DE-627 rakwb eng 610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Li, Junpeng verfasserin aut Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace 2021transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data. Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Elsevier Hua, Changchun oth Qian, Junlei oth Guan, Xinping oth Enthalten in Elsevier Polizzi, P. ELSEVIER Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species 2017transfer abstract [S.l.] (DE-627)ELV020637101 volume:421 year:2021 day:30 month:09 pages:178-192 extent:15 https://doi.org/10.1016/j.fss.2020.08.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-PHARM SSG-OLC-PHA SSG-OPC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_34 GBV_ILN_50 GBV_ILN_55 GBV_ILN_60 GBV_ILN_61 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_70 GBV_ILN_72 GBV_ILN_74 GBV_ILN_90 GBV_ILN_92 GBV_ILN_104 GBV_ILN_105 GBV_ILN_120 GBV_ILN_121 GBV_ILN_122 GBV_ILN_130 GBV_ILN_131 GBV_ILN_147 GBV_ILN_160 GBV_ILN_179 GBV_ILN_181 GBV_ILN_276 GBV_ILN_737 GBV_ILN_754 GBV_ILN_812 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2023 GBV_ILN_2024 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2032 GBV_ILN_2033 GBV_ILN_2035 GBV_ILN_2040 GBV_ILN_2043 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2065 GBV_ILN_2084 GBV_ILN_2121 GBV_ILN_2227 GBV_ILN_2502 GBV_ILN_2505 GBV_ILN_2508 44.40 Pharmazie Pharmazeutika VZ AR 421 2021 30 0930 178-192 15
language	English
source	Enthalten in Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species [S.l.] volume:421 year:2021 day:30 month:09 pages:178-192 extent:15
sourceStr	Enthalten in Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species [S.l.] volume:421 year:2021 day:30 month:09 pages:178-192 extent:15
format_phy_str_mv	Article
bklname	Pharmazie Pharmazeutika
institution	findex.gbv.de
topic_facet	Multi-Input Multi-Output Blast furnace Silicon content Takagi-Sugeno fuzzy model Molten iron quality
dewey-raw	610
isfreeaccess_bool	false
container_title	Blood cadmium and metallothionein concentrations in females of two sympatric pinnipeds species
authorswithroles_txt_mv	Li, Junpeng @@aut@@ Hua, Changchun @@oth@@ Qian, Junlei @@oth@@ Guan, Xinping @@oth@@
publishDateDaySort_date	2021-01-30T00:00:00Z
hierarchy_top_id	ELV020637101
dewey-sort	3610
id	ELV054927781
language_de	englisch
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Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. 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author	Li, Junpeng
spellingShingle	Li, Junpeng ddc 610 ssgn 15,3 fid PHARM bkl 44.40 Elsevier Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace
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topic_title	610 VZ 15,3 ssgn PHARM DE-84 fid 44.40 bkl Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace Multi-Input Multi-Output Elsevier Blast furnace Elsevier Silicon content Elsevier Takagi-Sugeno fuzzy model Elsevier Molten iron quality Elsevier
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title	Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace
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title_sort	low-rank based multi-input multi-output takagi-sugeno fuzzy modeling for prediction of molten iron quality in blast furnace
title_auth	Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace
abstract	For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data.
abstractGer	For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data.
abstract_unstemmed	For complex blast furnace smelting systems with large time delay, accurate prediction of molten iron quality indicators plays an important guiding role in blast furnace control. Recently, some data-driven Multi-Input Multi-Output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]). However, those data-driven MIMO models do not consider the inter-indicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. This paper proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model with taking full account of the inter-indicator correlation. In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. The validity of the proposed method is verified by simulation and comparison with some related methods on real blast furnace data.
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title_short	Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace
url	https://doi.org/10.1016/j.fss.2020.08.012
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up_date	2024-07-06T23:05:32.831Z
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In the novel method, the inter-indicator correlation was explicitly modeled by a low-rank learning in a latent space that overcame the great challenge of jointly determining the fuzzy rules of MIMO T-S model and the inter-indicator correlation. For the corresponding optimization problem, an effective alternating optimization algorithm is presented. 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Low-rank based Multi-Input Multi-Output Takagi-Sugeno fuzzy modeling for prediction of molten iron quality in blast furnace

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