Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling
We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and in...
Ausführliche Beschreibung
Autor*in: |
Ji, Rui [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: |
11 |
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Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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Übergeordnetes Werk: |
volume:123 ; year:2014 ; day:10 ; month:01 ; pages:281-291 ; extent:11 |
Links: |
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DOI / URN: |
10.1016/j.neucom.2013.07.017 |
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Katalog-ID: |
ELV017632803 |
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520 | |a We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. | ||
520 | |a We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. | ||
650 | 7 | |a Incremental learning |2 Elsevier | |
650 | 7 | |a Fuzzy modeling |2 Elsevier | |
650 | 7 | |a ε -insensitive learning |2 Elsevier | |
650 | 7 | |a Takagi–Sugeno fuzzy systems |2 Elsevier | |
650 | 7 | |a Smooth support vector regression |2 Elsevier | |
650 | 7 | |a Reference functions |2 Elsevier | |
700 | 1 | |a Yang, Yupu |4 oth | |
700 | 1 | |a Zhang, Weidong |4 oth | |
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2014transfer abstract |
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35.70 42.12 |
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2014 |
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10.1016/j.neucom.2013.07.017 doi GBVA2014014000023.pica (DE-627)ELV017632803 (ELSEVIER)S0925-2312(13)00759-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Ji, Rui verfasserin aut Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Elsevier Yang, Yupu oth Zhang, Weidong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 https://doi.org/10.1016/j.neucom.2013.07.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 123 2014 10 0110 281-291 11 045F 610 |
spelling |
10.1016/j.neucom.2013.07.017 doi GBVA2014014000023.pica (DE-627)ELV017632803 (ELSEVIER)S0925-2312(13)00759-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Ji, Rui verfasserin aut Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Elsevier Yang, Yupu oth Zhang, Weidong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 https://doi.org/10.1016/j.neucom.2013.07.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 123 2014 10 0110 281-291 11 045F 610 |
allfields_unstemmed |
10.1016/j.neucom.2013.07.017 doi GBVA2014014000023.pica (DE-627)ELV017632803 (ELSEVIER)S0925-2312(13)00759-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Ji, Rui verfasserin aut Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Elsevier Yang, Yupu oth Zhang, Weidong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 https://doi.org/10.1016/j.neucom.2013.07.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 123 2014 10 0110 281-291 11 045F 610 |
allfieldsGer |
10.1016/j.neucom.2013.07.017 doi GBVA2014014000023.pica (DE-627)ELV017632803 (ELSEVIER)S0925-2312(13)00759-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Ji, Rui verfasserin aut Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Elsevier Yang, Yupu oth Zhang, Weidong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 https://doi.org/10.1016/j.neucom.2013.07.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 123 2014 10 0110 281-291 11 045F 610 |
allfieldsSound |
10.1016/j.neucom.2013.07.017 doi GBVA2014014000023.pica (DE-627)ELV017632803 (ELSEVIER)S0925-2312(13)00759-5 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Ji, Rui verfasserin aut Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling 2014transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Elsevier Yang, Yupu oth Zhang, Weidong oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 https://doi.org/10.1016/j.neucom.2013.07.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 123 2014 10 0110 281-291 11 045F 610 |
language |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:123 year:2014 day:10 month:01 pages:281-291 extent:11 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. |
abstractGer |
We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. |
abstract_unstemmed |
We propose an architecture for Takagi–Sugeno (TS) fuzzy system and develop an incremental smooth support vector regression (ISSVR) algorithm to build the TS fuzzy system. ISSVR is based on the ε -insensitive smooth support vector regression ( ε -SSVR), a smoothing strategy for solving ε -SVR, and incremental reduced support vector machine (RSVM). The ISSVR incrementally selects representative samples from the given dataset as support vectors. We show that TS fuzzy modeling is equivalent to the ISSVR problem under certain assumptions. A TS fuzzy system can be generated from the given training data based on the ISSVR learning with each fuzzy rule given by a support vector. Compared with other fuzzy modeling methods, more forms of membership functions can be used in our model, and the number of fuzzy rules of our model is much smaller. The performance of our model is illustrated by extensive experiments and comparisons. |
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Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
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