Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm
Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Van Kien, Cao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
Differential evolution (DE) algorithm Nonlinear double-coupled fluid tank system |
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| Anmerkung: |
© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of fuzzy systems - Taibei : Association, 2006, 20(2017), 5 vom: 05. Dez., Seite 1671-1684 |
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| Übergeordnetes Werk: |
volume:20 ; year:2017 ; number:5 ; day:05 ; month:12 ; pages:1671-1684 |
| Links: |
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| DOI / URN: |
10.1007/s40815-017-0431-x |
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| Katalog-ID: |
SPR037858742 |
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| 100 | 1 | |a Van Kien, Cao |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm |
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| 520 | |a Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. | ||
| 650 | 4 | |a Multilayer fuzzy model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cascade training |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Differential evolution (DE) algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nonlinear double-coupled fluid tank system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiple-inputs multiple-outputs (MIMO) system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fuzzy Takagi–Sugeno (T–S) model |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Anh, Ho Pham Huy |4 aut | |
| 700 | 1 | |a Nam, Nguyen Thanh |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of fuzzy systems |d Taibei : Association, 2006 |g 20(2017), 5 vom: 05. Dez., Seite 1671-1684 |w (DE-627)612134636 |w (DE-600)2523322-1 |x 2199-3211 |7 nnns |
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10.1007/s40815-017-0431-x doi (DE-627)SPR037858742 (SPR)s40815-017-0431-x-e DE-627 ger DE-627 rakwb eng Van Kien, Cao verfasserin aut Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. Multilayer fuzzy model (dpeaa)DE-He213 Cascade training (dpeaa)DE-He213 Differential evolution (DE) algorithm (dpeaa)DE-He213 Nonlinear double-coupled fluid tank system (dpeaa)DE-He213 Multiple-inputs multiple-outputs (MIMO) system (dpeaa)DE-He213 Fuzzy Takagi–Sugeno (T–S) model (dpeaa)DE-He213 Anh, Ho Pham Huy aut Nam, Nguyen Thanh aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 5 vom: 05. Dez., Seite 1671-1684 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:5 day:05 month:12 pages:1671-1684 https://dx.doi.org/10.1007/s40815-017-0431-x 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 20 2017 5 05 12 1671-1684 |
| spelling |
10.1007/s40815-017-0431-x doi (DE-627)SPR037858742 (SPR)s40815-017-0431-x-e DE-627 ger DE-627 rakwb eng Van Kien, Cao verfasserin aut Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. Multilayer fuzzy model (dpeaa)DE-He213 Cascade training (dpeaa)DE-He213 Differential evolution (DE) algorithm (dpeaa)DE-He213 Nonlinear double-coupled fluid tank system (dpeaa)DE-He213 Multiple-inputs multiple-outputs (MIMO) system (dpeaa)DE-He213 Fuzzy Takagi–Sugeno (T–S) model (dpeaa)DE-He213 Anh, Ho Pham Huy aut Nam, Nguyen Thanh aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 5 vom: 05. Dez., Seite 1671-1684 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:5 day:05 month:12 pages:1671-1684 https://dx.doi.org/10.1007/s40815-017-0431-x 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 20 2017 5 05 12 1671-1684 |
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10.1007/s40815-017-0431-x doi (DE-627)SPR037858742 (SPR)s40815-017-0431-x-e DE-627 ger DE-627 rakwb eng Van Kien, Cao verfasserin aut Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. Multilayer fuzzy model (dpeaa)DE-He213 Cascade training (dpeaa)DE-He213 Differential evolution (DE) algorithm (dpeaa)DE-He213 Nonlinear double-coupled fluid tank system (dpeaa)DE-He213 Multiple-inputs multiple-outputs (MIMO) system (dpeaa)DE-He213 Fuzzy Takagi–Sugeno (T–S) model (dpeaa)DE-He213 Anh, Ho Pham Huy aut Nam, Nguyen Thanh aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 5 vom: 05. Dez., Seite 1671-1684 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:5 day:05 month:12 pages:1671-1684 https://dx.doi.org/10.1007/s40815-017-0431-x 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 20 2017 5 05 12 1671-1684 |
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10.1007/s40815-017-0431-x doi (DE-627)SPR037858742 (SPR)s40815-017-0431-x-e DE-627 ger DE-627 rakwb eng Van Kien, Cao verfasserin aut Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. Multilayer fuzzy model (dpeaa)DE-He213 Cascade training (dpeaa)DE-He213 Differential evolution (DE) algorithm (dpeaa)DE-He213 Nonlinear double-coupled fluid tank system (dpeaa)DE-He213 Multiple-inputs multiple-outputs (MIMO) system (dpeaa)DE-He213 Fuzzy Takagi–Sugeno (T–S) model (dpeaa)DE-He213 Anh, Ho Pham Huy aut Nam, Nguyen Thanh aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 5 vom: 05. Dez., Seite 1671-1684 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:5 day:05 month:12 pages:1671-1684 https://dx.doi.org/10.1007/s40815-017-0431-x 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 20 2017 5 05 12 1671-1684 |
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10.1007/s40815-017-0431-x doi (DE-627)SPR037858742 (SPR)s40815-017-0431-x-e DE-627 ger DE-627 rakwb eng Van Kien, Cao verfasserin aut Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. Multilayer fuzzy model (dpeaa)DE-He213 Cascade training (dpeaa)DE-He213 Differential evolution (DE) algorithm (dpeaa)DE-He213 Nonlinear double-coupled fluid tank system (dpeaa)DE-He213 Multiple-inputs multiple-outputs (MIMO) system (dpeaa)DE-He213 Fuzzy Takagi–Sugeno (T–S) model (dpeaa)DE-He213 Anh, Ho Pham Huy aut Nam, Nguyen Thanh aut Enthalten in International journal of fuzzy systems Taibei : Association, 2006 20(2017), 5 vom: 05. Dez., Seite 1671-1684 (DE-627)612134636 (DE-600)2523322-1 2199-3211 nnns volume:20 year:2017 number:5 day:05 month:12 pages:1671-1684 https://dx.doi.org/10.1007/s40815-017-0431-x 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 20 2017 5 05 12 1671-1684 |
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The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multilayer fuzzy model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cascade training</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Differential evolution (DE) algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nonlinear double-coupled fluid tank system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiple-inputs multiple-outputs (MIMO) system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy Takagi–Sugeno (T–S) model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Anh, Ho Pham Huy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nam, Nguyen Thanh</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of fuzzy systems</subfield><subfield code="d">Taibei : Association, 2006</subfield><subfield code="g">20(2017), 5 vom: 05. 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Van Kien, Cao misc Multilayer fuzzy model misc Cascade training misc Differential evolution (DE) algorithm misc Nonlinear double-coupled fluid tank system misc Multiple-inputs multiple-outputs (MIMO) system misc Fuzzy Takagi–Sugeno (T–S) model Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm |
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Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm Multilayer fuzzy model (dpeaa)DE-He213 Cascade training (dpeaa)DE-He213 Differential evolution (DE) algorithm (dpeaa)DE-He213 Nonlinear double-coupled fluid tank system (dpeaa)DE-He213 Multiple-inputs multiple-outputs (MIMO) system (dpeaa)DE-He213 Fuzzy Takagi–Sugeno (T–S) model (dpeaa)DE-He213 |
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cascade training multilayer fuzzy model for nonlinear uncertain system identification optimized by differential evolution algorithm |
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Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm |
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Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
| abstractGer |
Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
| abstract_unstemmed |
Abstract This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi–Sugeno (T–S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems. © Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature 2017 |
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Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm |
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Anh, Ho Pham Huy Nam, Nguyen Thanh |
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10.1007/s40815-017-0431-x |
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2024-07-03T14:46:24.826Z |
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| score |
7.4010143 |