Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler
This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networ...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Guoqiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing - 2012, the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, Amsterdam |
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| Übergeordnetes Werk: |
volume:51 ; year:2014 ; pages:57-66 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.neunet.2013.12.006 |
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| Katalog-ID: |
ELV034012796 |
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| 520 | |a This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. | ||
| 520 | |a This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. | ||
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10.1016/j.neunet.2013.12.006 doi GBVA2014015000004.pica (DE-627)ELV034012796 (ELSEVIER)S0893-6080(13)00300-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Li, Guoqiang verfasserin aut Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Least square Elsevier Coal-fired boiler Elsevier Artificial neural network Elsevier Least Square Fast Learning Network Elsevier Niu, Peifeng oth Wang, Huaibao oth Liu, Yongchao oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:51 year:2014 pages:57-66 extent:10 https://doi.org/10.1016/j.neunet.2013.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 51 2014 57-66 10 045F 004 |
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10.1016/j.neunet.2013.12.006 doi GBVA2014015000004.pica (DE-627)ELV034012796 (ELSEVIER)S0893-6080(13)00300-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Li, Guoqiang verfasserin aut Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Least square Elsevier Coal-fired boiler Elsevier Artificial neural network Elsevier Least Square Fast Learning Network Elsevier Niu, Peifeng oth Wang, Huaibao oth Liu, Yongchao oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:51 year:2014 pages:57-66 extent:10 https://doi.org/10.1016/j.neunet.2013.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 51 2014 57-66 10 045F 004 |
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10.1016/j.neunet.2013.12.006 doi GBVA2014015000004.pica (DE-627)ELV034012796 (ELSEVIER)S0893-6080(13)00300-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Li, Guoqiang verfasserin aut Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Least square Elsevier Coal-fired boiler Elsevier Artificial neural network Elsevier Least Square Fast Learning Network Elsevier Niu, Peifeng oth Wang, Huaibao oth Liu, Yongchao oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:51 year:2014 pages:57-66 extent:10 https://doi.org/10.1016/j.neunet.2013.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 51 2014 57-66 10 045F 004 |
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10.1016/j.neunet.2013.12.006 doi GBVA2014015000004.pica (DE-627)ELV034012796 (ELSEVIER)S0893-6080(13)00300-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Li, Guoqiang verfasserin aut Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Least square Elsevier Coal-fired boiler Elsevier Artificial neural network Elsevier Least Square Fast Learning Network Elsevier Niu, Peifeng oth Wang, Huaibao oth Liu, Yongchao oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:51 year:2014 pages:57-66 extent:10 https://doi.org/10.1016/j.neunet.2013.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 51 2014 57-66 10 045F 004 |
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10.1016/j.neunet.2013.12.006 doi GBVA2014015000004.pica (DE-627)ELV034012796 (ELSEVIER)S0893-6080(13)00300-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Li, Guoqiang verfasserin aut Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler 2014transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Least square Elsevier Coal-fired boiler Elsevier Artificial neural network Elsevier Least Square Fast Learning Network Elsevier Niu, Peifeng oth Wang, Huaibao oth Liu, Yongchao oth Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:51 year:2014 pages:57-66 extent:10 https://doi.org/10.1016/j.neunet.2013.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 51 2014 57-66 10 045F 004 |
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Enthalten in Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing Amsterdam volume:51 year:2014 pages:57-66 extent:10 |
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Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler |
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Li, Guoqiang |
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Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing |
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least square fast learning network for modeling the combustion efficiency of a 300wm coal-fired boiler |
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Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler |
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This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. |
| abstractGer |
This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. |
| abstract_unstemmed |
This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. |
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Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler |
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