An intelligent fractional-order system for the biological parameters regulations
Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sahu, Tapaswini [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of dynamics and control - Berlin : Springer, 2013, 11(2022), 4 vom: 31. Dez., Seite 1880-1894 |
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| Übergeordnetes Werk: |
volume:11 ; year:2022 ; number:4 ; day:31 ; month:12 ; pages:1880-1894 |
| Links: |
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| DOI / URN: |
10.1007/s40435-022-01088-z |
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SPR05194314X |
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| 520 | |a Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. | ||
| 650 | 4 | |a Bat optimization algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Biological system |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fractional-order controller |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tuning mechanism |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Tripathy, Madhab Chandra |4 aut | |
| 700 | 1 | |a Sahoo, Satya Prakash |4 aut | |
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10.1007/s40435-022-01088-z doi (DE-627)SPR05194314X (SPR)s40435-022-01088-z-e DE-627 ger DE-627 rakwb eng Sahu, Tapaswini verfasserin (orcid)0000-0001-8472-5129 aut An intelligent fractional-order system for the biological parameters regulations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. Bat optimization algorithm (dpeaa)DE-He213 Biological system (dpeaa)DE-He213 Fractional-order controller (dpeaa)DE-He213 Tuning mechanism (dpeaa)DE-He213 Tripathy, Madhab Chandra aut Sahoo, Satya Prakash aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 11(2022), 4 vom: 31. Dez., Seite 1880-1894 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:11 year:2022 number:4 day:31 month:12 pages:1880-1894 https://dx.doi.org/10.1007/s40435-022-01088-z 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_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 4 31 12 1880-1894 |
| spelling |
10.1007/s40435-022-01088-z doi (DE-627)SPR05194314X (SPR)s40435-022-01088-z-e DE-627 ger DE-627 rakwb eng Sahu, Tapaswini verfasserin (orcid)0000-0001-8472-5129 aut An intelligent fractional-order system for the biological parameters regulations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. Bat optimization algorithm (dpeaa)DE-He213 Biological system (dpeaa)DE-He213 Fractional-order controller (dpeaa)DE-He213 Tuning mechanism (dpeaa)DE-He213 Tripathy, Madhab Chandra aut Sahoo, Satya Prakash aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 11(2022), 4 vom: 31. Dez., Seite 1880-1894 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:11 year:2022 number:4 day:31 month:12 pages:1880-1894 https://dx.doi.org/10.1007/s40435-022-01088-z 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_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 4 31 12 1880-1894 |
| allfields_unstemmed |
10.1007/s40435-022-01088-z doi (DE-627)SPR05194314X (SPR)s40435-022-01088-z-e DE-627 ger DE-627 rakwb eng Sahu, Tapaswini verfasserin (orcid)0000-0001-8472-5129 aut An intelligent fractional-order system for the biological parameters regulations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. Bat optimization algorithm (dpeaa)DE-He213 Biological system (dpeaa)DE-He213 Fractional-order controller (dpeaa)DE-He213 Tuning mechanism (dpeaa)DE-He213 Tripathy, Madhab Chandra aut Sahoo, Satya Prakash aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 11(2022), 4 vom: 31. Dez., Seite 1880-1894 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:11 year:2022 number:4 day:31 month:12 pages:1880-1894 https://dx.doi.org/10.1007/s40435-022-01088-z 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_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 4 31 12 1880-1894 |
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10.1007/s40435-022-01088-z doi (DE-627)SPR05194314X (SPR)s40435-022-01088-z-e DE-627 ger DE-627 rakwb eng Sahu, Tapaswini verfasserin (orcid)0000-0001-8472-5129 aut An intelligent fractional-order system for the biological parameters regulations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. Bat optimization algorithm (dpeaa)DE-He213 Biological system (dpeaa)DE-He213 Fractional-order controller (dpeaa)DE-He213 Tuning mechanism (dpeaa)DE-He213 Tripathy, Madhab Chandra aut Sahoo, Satya Prakash aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 11(2022), 4 vom: 31. Dez., Seite 1880-1894 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:11 year:2022 number:4 day:31 month:12 pages:1880-1894 https://dx.doi.org/10.1007/s40435-022-01088-z 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_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 4 31 12 1880-1894 |
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10.1007/s40435-022-01088-z doi (DE-627)SPR05194314X (SPR)s40435-022-01088-z-e DE-627 ger DE-627 rakwb eng Sahu, Tapaswini verfasserin (orcid)0000-0001-8472-5129 aut An intelligent fractional-order system for the biological parameters regulations 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. Bat optimization algorithm (dpeaa)DE-He213 Biological system (dpeaa)DE-He213 Fractional-order controller (dpeaa)DE-He213 Tuning mechanism (dpeaa)DE-He213 Tripathy, Madhab Chandra aut Sahoo, Satya Prakash aut Enthalten in International journal of dynamics and control Berlin : Springer, 2013 11(2022), 4 vom: 31. Dez., Seite 1880-1894 (DE-627)745617794 (DE-600)2714518-9 2195-2698 nnns volume:11 year:2022 number:4 day:31 month:12 pages:1880-1894 https://dx.doi.org/10.1007/s40435-022-01088-z 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_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 4 31 12 1880-1894 |
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Sahu, Tapaswini |
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Sahu, Tapaswini misc Bat optimization algorithm misc Biological system misc Fractional-order controller misc Tuning mechanism An intelligent fractional-order system for the biological parameters regulations |
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An intelligent fractional-order system for the biological parameters regulations Bat optimization algorithm (dpeaa)DE-He213 Biological system (dpeaa)DE-He213 Fractional-order controller (dpeaa)DE-He213 Tuning mechanism (dpeaa)DE-He213 |
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An intelligent fractional-order system for the biological parameters regulations |
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Sahu, Tapaswini Tripathy, Madhab Chandra Sahoo, Satya Prakash |
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intelligent fractional-order system for the biological parameters regulations |
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An intelligent fractional-order system for the biological parameters regulations |
| abstract |
Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the desired level. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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2024-07-04T00:32:55.863Z |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate; sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. 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