Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables
Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hesamian, Gholamreza [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Taiwan Fuzzy Systems Association 2019 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 21(2019), 4 vom: 15. März, Seite 1187-1195 |
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| Übergeordnetes Werk: |
volume:21 ; year:2019 ; number:4 ; day:15 ; month:03 ; pages:1187-1195 |
| Links: |
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| DOI / URN: |
10.1007/s40815-019-00610-4 |
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| Katalog-ID: |
SPR037860631 |
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| 245 | 1 | 0 | |a Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables |
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| 520 | |a Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. | ||
| 650 | 4 | |a Normal fuzzy random variable |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fuzzy EWMA statistic |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fuzzy EWMA chart |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Consistent estimator |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Violence degree |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Akbari, Mohammad Ghasem |4 aut | |
| 700 | 1 | |a Ranjbar, Elham |4 aut | |
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| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s40815-019-00610-4 |z lizenzpflichtig |3 Volltext |
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10.1007/s40815-019-00610-4 doi (DE-627)SPR037860631 (SPR)s40815-019-00610-4-e DE-627 ger DE-627 rakwb eng Hesamian, Gholamreza verfasserin aut Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2019 Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. Normal fuzzy random variable (dpeaa)DE-He213 Fuzzy EWMA statistic (dpeaa)DE-He213 Fuzzy EWMA chart (dpeaa)DE-He213 Consistent estimator (dpeaa)DE-He213 Violence degree (dpeaa)DE-He213 Akbari, Mohammad Ghasem aut Ranjbar, Elham aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 21(2019), 4 vom: 15. März, Seite 1187-1195 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:21 year:2019 number:4 day:15 month:03 pages:1187-1195 https://dx.doi.org/10.1007/s40815-019-00610-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2019 4 15 03 1187-1195 |
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10.1007/s40815-019-00610-4 doi (DE-627)SPR037860631 (SPR)s40815-019-00610-4-e DE-627 ger DE-627 rakwb eng Hesamian, Gholamreza verfasserin aut Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2019 Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. Normal fuzzy random variable (dpeaa)DE-He213 Fuzzy EWMA statistic (dpeaa)DE-He213 Fuzzy EWMA chart (dpeaa)DE-He213 Consistent estimator (dpeaa)DE-He213 Violence degree (dpeaa)DE-He213 Akbari, Mohammad Ghasem aut Ranjbar, Elham aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 21(2019), 4 vom: 15. März, Seite 1187-1195 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:21 year:2019 number:4 day:15 month:03 pages:1187-1195 https://dx.doi.org/10.1007/s40815-019-00610-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2019 4 15 03 1187-1195 |
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10.1007/s40815-019-00610-4 doi (DE-627)SPR037860631 (SPR)s40815-019-00610-4-e DE-627 ger DE-627 rakwb eng Hesamian, Gholamreza verfasserin aut Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2019 Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. Normal fuzzy random variable (dpeaa)DE-He213 Fuzzy EWMA statistic (dpeaa)DE-He213 Fuzzy EWMA chart (dpeaa)DE-He213 Consistent estimator (dpeaa)DE-He213 Violence degree (dpeaa)DE-He213 Akbari, Mohammad Ghasem aut Ranjbar, Elham aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 21(2019), 4 vom: 15. März, Seite 1187-1195 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:21 year:2019 number:4 day:15 month:03 pages:1187-1195 https://dx.doi.org/10.1007/s40815-019-00610-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2019 4 15 03 1187-1195 |
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10.1007/s40815-019-00610-4 doi (DE-627)SPR037860631 (SPR)s40815-019-00610-4-e DE-627 ger DE-627 rakwb eng Hesamian, Gholamreza verfasserin aut Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2019 Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. Normal fuzzy random variable (dpeaa)DE-He213 Fuzzy EWMA statistic (dpeaa)DE-He213 Fuzzy EWMA chart (dpeaa)DE-He213 Consistent estimator (dpeaa)DE-He213 Violence degree (dpeaa)DE-He213 Akbari, Mohammad Ghasem aut Ranjbar, Elham aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 21(2019), 4 vom: 15. März, Seite 1187-1195 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:21 year:2019 number:4 day:15 month:03 pages:1187-1195 https://dx.doi.org/10.1007/s40815-019-00610-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2019 4 15 03 1187-1195 |
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10.1007/s40815-019-00610-4 doi (DE-627)SPR037860631 (SPR)s40815-019-00610-4-e DE-627 ger DE-627 rakwb eng Hesamian, Gholamreza verfasserin aut Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association 2019 Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. Normal fuzzy random variable (dpeaa)DE-He213 Fuzzy EWMA statistic (dpeaa)DE-He213 Fuzzy EWMA chart (dpeaa)DE-He213 Consistent estimator (dpeaa)DE-He213 Violence degree (dpeaa)DE-He213 Akbari, Mohammad Ghasem aut Ranjbar, Elham aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 21(2019), 4 vom: 15. März, Seite 1187-1195 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:21 year:2019 number:4 day:15 month:03 pages:1187-1195 https://dx.doi.org/10.1007/s40815-019-00610-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 21 2019 4 15 03 1187-1195 |
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Hesamian, Gholamreza |
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Hesamian, Gholamreza misc Normal fuzzy random variable misc Fuzzy EWMA statistic misc Fuzzy EWMA chart misc Consistent estimator misc Violence degree Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables |
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Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables Normal fuzzy random variable (dpeaa)DE-He213 Fuzzy EWMA statistic (dpeaa)DE-He213 Fuzzy EWMA chart (dpeaa)DE-He213 Consistent estimator (dpeaa)DE-He213 Violence degree (dpeaa)DE-He213 |
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Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables |
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Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables |
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exponentially weighted moving average control chart based on normal fuzzy random variables |
| title_auth |
Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables |
| abstract |
Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. © Taiwan Fuzzy Systems Association 2019 |
| abstractGer |
Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. © Taiwan Fuzzy Systems Association 2019 |
| abstract_unstemmed |
Abstract Exponentially weighted moving average (EWMA) chart is an alternative to Shewhart control charts and can serve as an effective tool for detection of shifts in small persistent process. Notably, existing methods rely on induced imprecise observations of a normal distribution with fuzzy mean and variance. Such techniques did not investigate the statistical properties relevant to a fuzzy EWMA. To overcome this shortcoming, employing a common notion of normal fuzzy random variable with fuzzy mean and non-fuzzy variance could be helpful. This paper first developed a notion of fuzzy EWMA statistic as a natural extension to the classical counterpart. Then, the concept of fuzzy EWMA control limit was introduced and discussed in cases where fuzzy mean and/or non-fuzzy variance was unknown parameters. A degree of violence was also employed to monitor the proposed fuzzy EWMA control chart. Potential applications of the proposed fuzzy EWMA chart were also demonstrated based on a real-life example. The advantages of the proposed method were also discussed in comparison with other existing fuzzy EWMA methods. © Taiwan Fuzzy Systems Association 2019 |
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Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables |
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https://dx.doi.org/10.1007/s40815-019-00610-4 |
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Akbari, Mohammad Ghasem Ranjbar, Elham |
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Akbari, Mohammad Ghasem Ranjbar, Elham |
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| doi_str |
10.1007/s40815-019-00610-4 |
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2024-07-03T14:47:11.426Z |
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