One-step models for soft computing techniques. Industrial application to image processing in quality assurance process
Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation proble...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dorantes, Pascual Noradino Montes [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2015 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 81(2015), 5-8 vom: 13. Mai, Seite 771-778 |
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| Übergeordnetes Werk: |
volume:81 ; year:2015 ; number:5-8 ; day:13 ; month:05 ; pages:771-778 |
| Links: |
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| DOI / URN: |
10.1007/s00170-015-7101-7 |
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SPR001872753 |
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| 520 | |a Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. | ||
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| 650 | 4 | |a Radial basis function network (RBFN) |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Gómez, Marco Aurelio Jiménez |4 aut | |
| 700 | 1 | |a Méndez, Gerardo Maximiliano |4 aut | |
| 700 | 1 | |a González, Juan Pablo Nieto |4 aut | |
| 700 | 1 | |a Elizondo, Jesús de la Rosa |4 aut | |
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10.1007/s00170-015-7101-7 doi (DE-627)SPR001872753 (SPR)s00170-015-7101-7-e DE-627 ger DE-627 rakwb eng Dorantes, Pascual Noradino Montes verfasserin aut One-step models for soft computing techniques. Industrial application to image processing in quality assurance process 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. Fuzzy systems (dpeaa)DE-He213 Non-singleton (T1NSFLS) (dpeaa)DE-He213 Adaptive neuro-fuzzy inference systems (ANFIS) (dpeaa)DE-He213 Radial basis function network (RBFN) (dpeaa)DE-He213 Gómez, Marco Aurelio Jiménez aut Méndez, Gerardo Maximiliano aut González, Juan Pablo Nieto aut Elizondo, Jesús de la Rosa aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 13. Mai, Seite 771-778 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:13 month:05 pages:771-778 https://dx.doi.org/10.1007/s00170-015-7101-7 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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 81 2015 5-8 13 05 771-778 |
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10.1007/s00170-015-7101-7 doi (DE-627)SPR001872753 (SPR)s00170-015-7101-7-e DE-627 ger DE-627 rakwb eng Dorantes, Pascual Noradino Montes verfasserin aut One-step models for soft computing techniques. Industrial application to image processing in quality assurance process 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. Fuzzy systems (dpeaa)DE-He213 Non-singleton (T1NSFLS) (dpeaa)DE-He213 Adaptive neuro-fuzzy inference systems (ANFIS) (dpeaa)DE-He213 Radial basis function network (RBFN) (dpeaa)DE-He213 Gómez, Marco Aurelio Jiménez aut Méndez, Gerardo Maximiliano aut González, Juan Pablo Nieto aut Elizondo, Jesús de la Rosa aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 13. Mai, Seite 771-778 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:13 month:05 pages:771-778 https://dx.doi.org/10.1007/s00170-015-7101-7 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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 81 2015 5-8 13 05 771-778 |
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10.1007/s00170-015-7101-7 doi (DE-627)SPR001872753 (SPR)s00170-015-7101-7-e DE-627 ger DE-627 rakwb eng Dorantes, Pascual Noradino Montes verfasserin aut One-step models for soft computing techniques. Industrial application to image processing in quality assurance process 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. Fuzzy systems (dpeaa)DE-He213 Non-singleton (T1NSFLS) (dpeaa)DE-He213 Adaptive neuro-fuzzy inference systems (ANFIS) (dpeaa)DE-He213 Radial basis function network (RBFN) (dpeaa)DE-He213 Gómez, Marco Aurelio Jiménez aut Méndez, Gerardo Maximiliano aut González, Juan Pablo Nieto aut Elizondo, Jesús de la Rosa aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 13. Mai, Seite 771-778 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:13 month:05 pages:771-778 https://dx.doi.org/10.1007/s00170-015-7101-7 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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 81 2015 5-8 13 05 771-778 |
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10.1007/s00170-015-7101-7 doi (DE-627)SPR001872753 (SPR)s00170-015-7101-7-e DE-627 ger DE-627 rakwb eng Dorantes, Pascual Noradino Montes verfasserin aut One-step models for soft computing techniques. Industrial application to image processing in quality assurance process 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. Fuzzy systems (dpeaa)DE-He213 Non-singleton (T1NSFLS) (dpeaa)DE-He213 Adaptive neuro-fuzzy inference systems (ANFIS) (dpeaa)DE-He213 Radial basis function network (RBFN) (dpeaa)DE-He213 Gómez, Marco Aurelio Jiménez aut Méndez, Gerardo Maximiliano aut González, Juan Pablo Nieto aut Elizondo, Jesús de la Rosa aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 13. Mai, Seite 771-778 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:13 month:05 pages:771-778 https://dx.doi.org/10.1007/s00170-015-7101-7 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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 81 2015 5-8 13 05 771-778 |
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10.1007/s00170-015-7101-7 doi (DE-627)SPR001872753 (SPR)s00170-015-7101-7-e DE-627 ger DE-627 rakwb eng Dorantes, Pascual Noradino Montes verfasserin aut One-step models for soft computing techniques. Industrial application to image processing in quality assurance process 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2015 Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. Fuzzy systems (dpeaa)DE-He213 Non-singleton (T1NSFLS) (dpeaa)DE-He213 Adaptive neuro-fuzzy inference systems (ANFIS) (dpeaa)DE-He213 Radial basis function network (RBFN) (dpeaa)DE-He213 Gómez, Marco Aurelio Jiménez aut Méndez, Gerardo Maximiliano aut González, Juan Pablo Nieto aut Elizondo, Jesús de la Rosa aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 81(2015), 5-8 vom: 13. Mai, Seite 771-778 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:81 year:2015 number:5-8 day:13 month:05 pages:771-778 https://dx.doi.org/10.1007/s00170-015-7101-7 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_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_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 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_2056 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 81 2015 5-8 13 05 771-778 |
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Dorantes, Pascual Noradino Montes @@aut@@ Gómez, Marco Aurelio Jiménez @@aut@@ Méndez, Gerardo Maximiliano @@aut@@ González, Juan Pablo Nieto @@aut@@ Elizondo, Jesús de la Rosa @@aut@@ |
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Industrial application to image processing in quality assurance process</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</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">© Springer-Verlag London 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy systems</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-singleton (T1NSFLS)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive neuro-fuzzy inference systems (ANFIS)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Radial basis function network (RBFN)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gómez, Marco Aurelio Jiménez</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Méndez, Gerardo Maximiliano</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">González, Juan Pablo Nieto</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Elizondo, Jesús de la Rosa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of advanced manufacturing technology</subfield><subfield code="d">London : Springer, 1985</subfield><subfield code="g">81(2015), 5-8 vom: 13. 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Dorantes, Pascual Noradino Montes |
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Dorantes, Pascual Noradino Montes misc Fuzzy systems misc Non-singleton (T1NSFLS) misc Adaptive neuro-fuzzy inference systems (ANFIS) misc Radial basis function network (RBFN) One-step models for soft computing techniques. Industrial application to image processing in quality assurance process |
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One-step models for soft computing techniques. Industrial application to image processing in quality assurance process Fuzzy systems (dpeaa)DE-He213 Non-singleton (T1NSFLS) (dpeaa)DE-He213 Adaptive neuro-fuzzy inference systems (ANFIS) (dpeaa)DE-He213 Radial basis function network (RBFN) (dpeaa)DE-He213 |
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Dorantes, Pascual Noradino Montes Gómez, Marco Aurelio Jiménez Méndez, Gerardo Maximiliano González, Juan Pablo Nieto Elizondo, Jesús de la Rosa |
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one-step models for soft computing techniques. industrial application to image processing in quality assurance process |
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One-step models for soft computing techniques. Industrial application to image processing in quality assurance process |
| abstract |
Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. © Springer-Verlag London 2015 |
| abstractGer |
Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. © Springer-Verlag London 2015 |
| abstract_unstemmed |
Abstract The authors present an approach based on a one-step method using soft computing techniques for a quality assurance process in the form of dimensional checking parameters via industrial image processing. This method offers a high grade of precision in processes to solve the evaluation problems on-line applications. As well known to the researchers, the approaches in a single iteration of these techniques for artificial neural networks (ANN) such as adaptive neuro fuzzy inference systems (ANFIS) and radial basis function network (RBFN) are not documented in the literature. This work provides the simplification to one-step that provides the chance of creation and implementation of these models for online applications without loss of time in the iterations needed to adjust the model (training) to generate a fast response. The main objective of this paper is to provide a model capable of approximating the solution of a function that represents the system, that function is based on historical data of the process but the operators that compound the function are unknown. The relations between the inputs and outputs are known, but the interactions between variables are unknown. Based on the literature, the soft computing techniques are trained by trial and error because they do not have a stop criterion; also, the functions that provide an approximation are unknown in most cases. To solve the problem mentioned above, this paper proposes the one-step method without training. It is necessary to approximate the solution avoiding the overshoot and damping produced by classic approaches. The deviation generated is in the order of one standard deviation whose magnitude is in the order of common approaches for image processing as it is documented in literature for the best case RBFN. © Springer-Verlag London 2015 |
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| title_short |
One-step models for soft computing techniques. Industrial application to image processing in quality assurance process |
| url |
https://dx.doi.org/10.1007/s00170-015-7101-7 |
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| author2 |
Gómez, Marco Aurelio Jiménez Méndez, Gerardo Maximiliano González, Juan Pablo Nieto Elizondo, Jesús de la Rosa |
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Gómez, Marco Aurelio Jiménez Méndez, Gerardo Maximiliano González, Juan Pablo Nieto Elizondo, Jesús de la Rosa |
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10.1007/s00170-015-7101-7 |
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| score |
7.401143 |