Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals
Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter take...
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| Autor*in: |
Amadi, Chukwuemena Cyprian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 38(2019), 8 vom: 25. Jan., Seite 3860-3879 |
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| Übergeordnetes Werk: |
volume:38 ; year:2019 ; number:8 ; day:25 ; month:01 ; pages:3860-3879 |
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| DOI / URN: |
10.1007/s00034-019-01034-z |
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| Katalog-ID: |
SPR00059993X |
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| 100 | 1 | |a Amadi, Chukwuemena Cyprian |e verfasserin |0 (orcid)0000-0003-2009-2822 |4 aut | |
| 245 | 1 | 0 | |a Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals |
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| 520 | |a Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). | ||
| 650 | 4 | |a Widely nonlinear modeling |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Augmented complex statistics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Augmented (CNGD) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nonlinear systems |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Collaborative filters |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Wind modeling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ujang, Bukhari |4 aut | |
| 700 | 1 | |a Sali, A. |4 aut | |
| 700 | 1 | |a Hashim, Fazirulhisyam |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Circuits, systems and signal processing |d Boston, Mass. : Birkhäuser, 1982 |g 38(2019), 8 vom: 25. Jan., Seite 3860-3879 |w (DE-627)351975470 |w (DE-600)2085136-4 |x 1531-5878 |7 nnns |
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10.1007/s00034-019-01034-z doi (DE-627)SPR00059993X (SPR)s00034-019-01034-z-e DE-627 ger DE-627 rakwb eng Amadi, Chukwuemena Cyprian verfasserin (orcid)0000-0003-2009-2822 aut Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). Widely nonlinear modeling (dpeaa)DE-He213 Augmented complex statistics (dpeaa)DE-He213 Augmented (CNGD) (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Collaborative filters (dpeaa)DE-He213 Wind modeling (dpeaa)DE-He213 Ujang, Bukhari aut Sali, A. aut Hashim, Fazirulhisyam aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 25. Jan., Seite 3860-3879 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:25 month:01 pages:3860-3879 https://dx.doi.org/10.1007/s00034-019-01034-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_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_267 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 38 2019 8 25 01 3860-3879 |
| spelling |
10.1007/s00034-019-01034-z doi (DE-627)SPR00059993X (SPR)s00034-019-01034-z-e DE-627 ger DE-627 rakwb eng Amadi, Chukwuemena Cyprian verfasserin (orcid)0000-0003-2009-2822 aut Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). Widely nonlinear modeling (dpeaa)DE-He213 Augmented complex statistics (dpeaa)DE-He213 Augmented (CNGD) (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Collaborative filters (dpeaa)DE-He213 Wind modeling (dpeaa)DE-He213 Ujang, Bukhari aut Sali, A. aut Hashim, Fazirulhisyam aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 25. Jan., Seite 3860-3879 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:25 month:01 pages:3860-3879 https://dx.doi.org/10.1007/s00034-019-01034-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_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_267 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 38 2019 8 25 01 3860-3879 |
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10.1007/s00034-019-01034-z doi (DE-627)SPR00059993X (SPR)s00034-019-01034-z-e DE-627 ger DE-627 rakwb eng Amadi, Chukwuemena Cyprian verfasserin (orcid)0000-0003-2009-2822 aut Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). Widely nonlinear modeling (dpeaa)DE-He213 Augmented complex statistics (dpeaa)DE-He213 Augmented (CNGD) (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Collaborative filters (dpeaa)DE-He213 Wind modeling (dpeaa)DE-He213 Ujang, Bukhari aut Sali, A. aut Hashim, Fazirulhisyam aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 25. Jan., Seite 3860-3879 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:25 month:01 pages:3860-3879 https://dx.doi.org/10.1007/s00034-019-01034-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_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_267 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 38 2019 8 25 01 3860-3879 |
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10.1007/s00034-019-01034-z doi (DE-627)SPR00059993X (SPR)s00034-019-01034-z-e DE-627 ger DE-627 rakwb eng Amadi, Chukwuemena Cyprian verfasserin (orcid)0000-0003-2009-2822 aut Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). Widely nonlinear modeling (dpeaa)DE-He213 Augmented complex statistics (dpeaa)DE-He213 Augmented (CNGD) (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Collaborative filters (dpeaa)DE-He213 Wind modeling (dpeaa)DE-He213 Ujang, Bukhari aut Sali, A. aut Hashim, Fazirulhisyam aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 25. Jan., Seite 3860-3879 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:25 month:01 pages:3860-3879 https://dx.doi.org/10.1007/s00034-019-01034-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_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_267 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 38 2019 8 25 01 3860-3879 |
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10.1007/s00034-019-01034-z doi (DE-627)SPR00059993X (SPR)s00034-019-01034-z-e DE-627 ger DE-627 rakwb eng Amadi, Chukwuemena Cyprian verfasserin (orcid)0000-0003-2009-2822 aut Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). Widely nonlinear modeling (dpeaa)DE-He213 Augmented complex statistics (dpeaa)DE-He213 Augmented (CNGD) (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Collaborative filters (dpeaa)DE-He213 Wind modeling (dpeaa)DE-He213 Ujang, Bukhari aut Sali, A. aut Hashim, Fazirulhisyam aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 38(2019), 8 vom: 25. Jan., Seite 3860-3879 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:38 year:2019 number:8 day:25 month:01 pages:3860-3879 https://dx.doi.org/10.1007/s00034-019-01034-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_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_267 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 38 2019 8 25 01 3860-3879 |
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Amadi, Chukwuemena Cyprian @@aut@@ Ujang, Bukhari @@aut@@ Sali, A. @@aut@@ Hashim, Fazirulhisyam @@aut@@ |
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Amadi, Chukwuemena Cyprian |
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Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals Widely nonlinear modeling (dpeaa)DE-He213 Augmented complex statistics (dpeaa)DE-He213 Augmented (CNGD) (dpeaa)DE-He213 Nonlinear systems (dpeaa)DE-He213 Collaborative filters (dpeaa)DE-He213 Wind modeling (dpeaa)DE-He213 |
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collaborative adaptive filtering approach for the identification of complex-valued improper signals |
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Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals |
| abstract |
Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract This paper proposes a novel hybrid filter for data-adaptive optimal identification and modeling of complex-valued real-world signals based on the convex combination approach. It is equipped with different complex domain characteristics of subfilter algorithm. The proposed hybrid filter takes advantage of the complex nonlinear gradient descent (CNGD) algorithm that exhibits fast convergence and the steady state of the augmented complex nonlinear gradient descent (ACNGD) algorithm. The output of CNGD and ACNGD was combined to work in parallel, feeding each individual subfilter output into a mixing algorithm, which in the end produced a single hybrid filter output. The mixing parameter %$ \lambda \left( k \right) %$ within the hybrid filter architecture was made gradient adaptive in order to preserve the nature of inherent characteristics of the subfilters and to show its optimal performance in identifying and tracking second-order properness (circular) and improperness (noncircular) of the complex signals in real time. Further analysis was made on the properties of the algorithms, and the relationship between fast convergence and steady-state error was discussed. This analysis is supported by the complex-valued synthetic simulation and real-world application dataset as applied in renewable energy (wind). © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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Collaborative Adaptive Filtering Approach for the Identification of Complex-Valued Improper Signals |
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https://dx.doi.org/10.1007/s00034-019-01034-z |
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Ujang, Bukhari Sali, A. Hashim, Fazirulhisyam |
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Ujang, Bukhari Sali, A. Hashim, Fazirulhisyam |
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10.1007/s00034-019-01034-z |
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2024-07-03T17:12:26.983Z |
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| score |
7.399867 |