000 | 03625nam a22005535i 4500 | ||
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001 | 978-0-387-68630-1 | ||
003 | DE-He213 | ||
005 | 20201213203101.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2007 xxu| s |||| 0|eng d | ||
020 |
_a9780387686301 _9978-0-387-68630-1 |
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024 | 7 |
_a10.1007/978-0-387-68630-1 _2doi |
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050 | 4 | _aTK5102.9 | |
050 | 4 | _aTA1637-1638 | |
050 | 4 | _aTK7882.S65 | |
072 | 7 |
_aTTBM _2bicssc |
|
072 | 7 |
_aUYS _2bicssc |
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072 | 7 |
_aTEC008000 _2bisacsh |
|
072 | 7 |
_aCOM073000 _2bisacsh |
|
082 | 0 | 4 |
_a621.382 _223 |
100 | 1 |
_aOgunfunmi, Tokunbo. _eauthor. |
|
245 | 1 | 0 |
_aAdaptive Nonlinear System Identification _h[electronic resource] : _bThe Volterra and Wiener Model Approaches / _cby Tokunbo Ogunfunmi. |
264 | 1 |
_aBoston, MA : _bSpringer US, _c2007. |
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300 |
_aXVI, 232 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSignals And Communication Technology, _x1860-4862 |
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505 | 0 | _ato Nonlinear Systems -- Polynomial Models of Nonlinear Systems -- Volterra and Wiener Nonlinear Models -- Nonlinear System Identification Methods -- to Adaptive Signal Processing -- Nonlinear Adaptive System Identification Based on Volterra Models -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 1) -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 2) -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 3) -- Nonlinear Adaptive System Identification Based on Wiener Models (Part 4) -- Conclusions, Recent Results, and New Directions. | |
520 | _aAdaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches introduces engineers and researchers to the field of nonlinear adaptive system identification. The book includes recent research results in the area of adaptive nonlinear system identification and presents simple, concise, easy-to-understand methods for identifying nonlinear systems. These methods use adaptive filter algorithms that are well known for linear systems identification. They are applicable for nonlinear systems that can be efficiently modeled by polynomials. After a brief introduction to nonlinear systems and to adaptive system identification, the author presents the discrete Volterra model approach. This is followed by an explanation of the Wiener model approach. Adaptive algorithms using both models are developed. The performance of the two methods are then compared to determine which model performs better for system identification applications. Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches is useful to graduates students, engineers and researchers in the areas of nonlinear systems, control, biomedical systems and in adaptive signal processing. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aComputer vision. | |
650 | 0 | _aTelecommunication. | |
650 | 0 | _aSystems engineering. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aSignal, Image and Speech Processing. |
650 | 2 | 4 | _aControl, Robotics, Mechatronics. |
650 | 2 | 4 | _aImage Processing and Computer Vision. |
650 | 2 | 4 | _aCircuits and Systems. |
650 | 2 | 4 | _aCommunications Engineering, Networks. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9780387263281 |
830 | 0 |
_aSignals And Communication Technology, _x1860-4862 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-0-387-68630-1 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c20181 _d20181 |