Optimization in Identification of Logical-Probabilistic Risk Models
Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rybakov, A. V. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2003 |
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| Schlagwörter: |
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| Anmerkung: |
© MAIK “Nauka/Interperiodica” 2003 |
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| Übergeordnetes Werk: |
Enthalten in: Automation and remote control - Kluwer Academic Publishers-Plenum Publishers, 1957, 64(2003), 7 vom: Juli, Seite 1063-1073 |
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| Übergeordnetes Werk: |
volume:64 ; year:2003 ; number:7 ; month:07 ; pages:1063-1073 |
| Links: |
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| DOI / URN: |
10.1023/A:1024726000159 |
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OLC2060885841 |
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| 520 | |a Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. | ||
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10.1023/A:1024726000159 doi (DE-627)OLC2060885841 (DE-He213)A:1024726000159-p DE-627 ger DE-627 rakwb eng 000 620 VZ Rybakov, A. V. verfasserin aut Optimization in Identification of Logical-Probabilistic Risk Models 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2003 Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. Mechanical Engineer Computation Time System Theory Search Method Risk Model Solozhentsev, E. D. aut Enthalten in Automation and remote control Kluwer Academic Publishers-Plenum Publishers, 1957 64(2003), 7 vom: Juli, Seite 1063-1073 (DE-627)129603481 (DE-600)241725-X (DE-576)015097315 0005-1179 nnns volume:64 year:2003 number:7 month:07 pages:1063-1073 https://doi.org/10.1023/A:1024726000159 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4116 GBV_ILN_4700 AR 64 2003 7 07 1063-1073 |
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10.1023/A:1024726000159 doi (DE-627)OLC2060885841 (DE-He213)A:1024726000159-p DE-627 ger DE-627 rakwb eng 000 620 VZ Rybakov, A. V. verfasserin aut Optimization in Identification of Logical-Probabilistic Risk Models 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2003 Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. Mechanical Engineer Computation Time System Theory Search Method Risk Model Solozhentsev, E. D. aut Enthalten in Automation and remote control Kluwer Academic Publishers-Plenum Publishers, 1957 64(2003), 7 vom: Juli, Seite 1063-1073 (DE-627)129603481 (DE-600)241725-X (DE-576)015097315 0005-1179 nnns volume:64 year:2003 number:7 month:07 pages:1063-1073 https://doi.org/10.1023/A:1024726000159 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4116 GBV_ILN_4700 AR 64 2003 7 07 1063-1073 |
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10.1023/A:1024726000159 doi (DE-627)OLC2060885841 (DE-He213)A:1024726000159-p DE-627 ger DE-627 rakwb eng 000 620 VZ Rybakov, A. V. verfasserin aut Optimization in Identification of Logical-Probabilistic Risk Models 2003 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © MAIK “Nauka/Interperiodica” 2003 Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. Mechanical Engineer Computation Time System Theory Search Method Risk Model Solozhentsev, E. D. aut Enthalten in Automation and remote control Kluwer Academic Publishers-Plenum Publishers, 1957 64(2003), 7 vom: Juli, Seite 1063-1073 (DE-627)129603481 (DE-600)241725-X (DE-576)015097315 0005-1179 nnns volume:64 year:2003 number:7 month:07 pages:1063-1073 https://doi.org/10.1023/A:1024726000159 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4116 GBV_ILN_4700 AR 64 2003 7 07 1063-1073 |
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Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. © MAIK “Nauka/Interperiodica” 2003 |
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Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. © MAIK “Nauka/Interperiodica” 2003 |
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Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. The integral multi-extremal multi-parametric aim function of the optimization problem in investigated as a function of the number of optimizations, maximal and minimal amplitudes of increments of parameters, initial value of the aim function, choice of identical or different amplitudes of increments for different parameters, and risk distribution. An effective method for finding the global extremum in identifying a logical-probabilistic risk model within reasonable computation time is developed. © MAIK “Nauka/Interperiodica” 2003 |
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V.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimization in Identification of Logical-Probabilistic Risk Models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2003</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© MAIK “Nauka/Interperiodica” 2003</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Identification of logical-probabilistic risk models with groups of incompatible events is investigated by the random search method. 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