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
Gespeichert in:
| Autor*in: |
Ji, Rui [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
11 |
|---|
| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:123 ; year:2014 ; day:10 ; month:01 ; pages:281-291 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2013.07.017 |
|---|
| Katalog-ID: |
ELV017632803 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV017632803 | ||
| 003 | DE-627 | ||
| 005 | 20230625122600.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180602s2014 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.neucom.2013.07.017 |2 doi | |
| 028 | 5 | 2 | |a GBVA2014014000023.pica |
| 035 | |a (DE-627)ELV017632803 | ||
| 035 | |a (ELSEVIER)S0925-2312(13)00759-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 610 | |
| 082 | 0 | 4 | |a 610 |q DE-600 |
| 082 | 0 | 4 | |a 570 |q VZ |
| 084 | |a BIODIV |q DE-30 |2 fid | ||
| 084 | |a 35.70 |2 bkl | ||
| 084 | |a 42.12 |2 bkl | ||
| 100 | 1 | |a Ji, Rui |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
| 264 | 1 | |c 2014transfer abstract | |
| 300 | |a 11 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 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 | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
| 773 | 1 | 8 | |g volume:123 |g year:2014 |g day:10 |g month:01 |g pages:281-291 |g extent:11 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.neucom.2013.07.017 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a FID-BIODIV | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
| 936 | b | k | |a 42.12 |j Biophysik |q VZ |
| 951 | |a AR | ||
| 952 | |d 123 |j 2014 |b 10 |c 0110 |h 281-291 |g 11 | ||
| 953 | |2 045F |a 610 | ||
| author_variant |
r j rj |
|---|---|
| matchkey_str |
jiruiyangyupuzhangweidong:2014----:nrmnasotspotetrersinotkgs |
| hierarchy_sort_str |
2014transfer abstract |
| bklnumber |
35.70 42.12 |
| publishDate |
2014 |
| allfields |
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 |
English |
| source |
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 |
| sourceStr |
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 |
| format_phy_str_mv |
Article |
| bklname |
Biochemie: Allgemeines Biophysik |
| institution |
findex.gbv.de |
| topic_facet |
Incremental learning Fuzzy modeling ε -insensitive learning Takagi–Sugeno fuzzy systems Smooth support vector regression Reference functions |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| authorswithroles_txt_mv |
Ji, Rui @@aut@@ Yang, Yupu @@oth@@ Zhang, Weidong @@oth@@ |
| publishDateDaySort_date |
2014-01-10T00:00:00Z |
| hierarchy_top_id |
ELV002603926 |
| dewey-sort |
3610 |
| id |
ELV017632803 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV017632803</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625122600.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180602s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2013.07.017</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014014000023.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV017632803</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(13)00759-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">610</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ji, Rui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Incremental learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuzzy modeling</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ε -insensitive learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Takagi–Sugeno fuzzy systems</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Smooth support vector regression</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Reference functions</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Yupu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Weidong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:123</subfield><subfield code="g">year:2014</subfield><subfield code="g">day:10</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:281-291</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2013.07.017</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">123</subfield><subfield code="j">2014</subfield><subfield code="b">10</subfield><subfield code="c">0110</subfield><subfield code="h">281-291</subfield><subfield code="g">11</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">610</subfield></datafield></record></collection>
|
| author |
Ji, Rui |
| spellingShingle |
Ji, Rui ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
| authorStr |
Ji, Rui |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV002603926 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health 570 - Life sciences; biology |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions Elsevier |
| topic |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions |
| topic_unstemmed |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions |
| topic_browse |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Incremental learning Elsevier Fuzzy modeling Elsevier ε -insensitive learning Elsevier Takagi–Sugeno fuzzy systems Elsevier Smooth support vector regression Elsevier Reference functions |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
y y yy w z wz |
| hierarchy_parent_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| hierarchy_parent_id |
ELV002603926 |
| dewey-tens |
610 - Medicine & health 570 - Life sciences; biology |
| hierarchy_top_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV002603926 |
| title |
Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
| ctrlnum |
(DE-627)ELV017632803 (ELSEVIER)S0925-2312(13)00759-5 |
| title_full |
Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
| author_sort |
Ji, Rui |
| journal |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| journalStr |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology 500 - Science |
| recordtype |
marc |
| publishDateSort |
2014 |
| contenttype_str_mv |
zzz |
| container_start_page |
281 |
| author_browse |
Ji, Rui |
| container_volume |
123 |
| physical |
11 |
| class |
610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ji, Rui |
| doi_str_mv |
10.1016/j.neucom.2013.07.017 |
| dewey-full |
610 570 |
| title_sort |
incremental smooth support vector regression for takagi–sugeno fuzzy modeling |
| title_auth |
Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
| abstract |
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. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
| title_short |
Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling |
| url |
https://doi.org/10.1016/j.neucom.2013.07.017 |
| remote_bool |
true |
| author2 |
Yang, Yupu Zhang, Weidong |
| author2Str |
Yang, Yupu Zhang, Weidong |
| ppnlink |
ELV002603926 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| doi_str |
10.1016/j.neucom.2013.07.017 |
| up_date |
2024-07-06T16:48:24.797Z |
| _version_ |
1803849044904116224 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV017632803</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625122600.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180602s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2013.07.017</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2014014000023.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV017632803</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(13)00759-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">610</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ji, Rui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Incremental smooth support vector regression for Takagi–Sugeno fuzzy modeling</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">11</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Incremental learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fuzzy modeling</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ε -insensitive learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Takagi–Sugeno fuzzy systems</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Smooth support vector regression</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Reference functions</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Yupu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Weidong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:123</subfield><subfield code="g">year:2014</subfield><subfield code="g">day:10</subfield><subfield code="g">month:01</subfield><subfield code="g">pages:281-291</subfield><subfield code="g">extent:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2013.07.017</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">123</subfield><subfield code="j">2014</subfield><subfield code="b">10</subfield><subfield code="c">0110</subfield><subfield code="h">281-291</subfield><subfield code="g">11</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">610</subfield></datafield></record></collection>
|
| score |
7.4003057 |