Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering
Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to...
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| Autor*in: |
Zhao, Fang [verfasserIn] Guo, Hongyue [verfasserIn] Wang, Lidong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 25(2021), 14 vom: 04. Mai, Seite 9013-9023 |
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| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:14 ; day:04 ; month:05 ; pages:9013-9023 |
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| DOI / URN: |
10.1007/s00500-021-05828-9 |
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SPR044334192 |
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| 520 | |a Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. | ||
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10.1007/s00500-021-05828-9 doi (DE-627)SPR044334192 (SPR)s00500-021-05828-9-e DE-627 ger DE-627 rakwb eng Zhao, Fang verfasserin aut Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. Rule-based model (dpeaa)DE-He213 Fuzzy rules (dpeaa)DE-He213 Boundary erosion algorithm (dpeaa)DE-He213 Information granules (dpeaa)DE-He213 Guo, Hongyue verfasserin aut Wang, Lidong verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 04. Mai, Seite 9013-9023 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:04 month:05 pages:9013-9023 https://dx.doi.org/10.1007/s00500-021-05828-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 04 05 9013-9023 |
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10.1007/s00500-021-05828-9 doi (DE-627)SPR044334192 (SPR)s00500-021-05828-9-e DE-627 ger DE-627 rakwb eng Zhao, Fang verfasserin aut Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. Rule-based model (dpeaa)DE-He213 Fuzzy rules (dpeaa)DE-He213 Boundary erosion algorithm (dpeaa)DE-He213 Information granules (dpeaa)DE-He213 Guo, Hongyue verfasserin aut Wang, Lidong verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 04. Mai, Seite 9013-9023 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:04 month:05 pages:9013-9023 https://dx.doi.org/10.1007/s00500-021-05828-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 04 05 9013-9023 |
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10.1007/s00500-021-05828-9 doi (DE-627)SPR044334192 (SPR)s00500-021-05828-9-e DE-627 ger DE-627 rakwb eng Zhao, Fang verfasserin aut Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. Rule-based model (dpeaa)DE-He213 Fuzzy rules (dpeaa)DE-He213 Boundary erosion algorithm (dpeaa)DE-He213 Information granules (dpeaa)DE-He213 Guo, Hongyue verfasserin aut Wang, Lidong verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 25(2021), 14 vom: 04. Mai, Seite 9013-9023 (DE-627)SPR006469531 nnns volume:25 year:2021 number:14 day:04 month:05 pages:9013-9023 https://dx.doi.org/10.1007/s00500-021-05828-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 25 2021 14 04 05 9013-9023 |
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Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR044334192</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210619064840.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210619s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-021-05828-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR044334192</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-021-05828-9-e</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="100" ind1="1" ind2=" "><subfield code="a">Zhao, Fang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Granular rule-based modeling using the principle of justifiable granularity and boundary erosion clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Rule-based models constructed by “IF-THEN” fuzzy rules are commonly used in a complex and nonlinear system. In this study, a novel modeling method is established to generate fuzzy rules based on experimental evidence. Such modeling is realized by utilizing the boundary erosion algorithm to cluster the input samples and the principle of justifiable granularity to granulate the corresponding output. To further examine the performance of the designed rule-based model under different granularity levels, a model with the finer information granules is designed for rule extraction in each cluster. The proposed models are assessed on the synthetic and ship datasets, where the comparison between the granular output and the original data value is considered as the evaluation metric based on the converge and specificity of information granules. Numerical results show that the rule-based models, which incorporate information granules to form representative rules, perform better in analyzing the structure of the arbitrary-shaped datasets and offer a potential application in ship management.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rule-based model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy rules</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Boundary erosion algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information granules</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Hongyue</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Lidong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">25(2021), 14 vom: 04. 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