東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Aircraft aerodynamic parameter estim...

Mohamed, Majeed.

FindBook

Google Book

Amazon

博客來

Aircraft aerodynamic parameter estimation from flight data using neural partial differentiation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Aircraft aerodynamic parameter estimation from flight data using neural partial differentiation/ by Majeed Mohamed, Vikalp Dongare.
作者:	Mohamed, Majeed.
其他作者:	Dongare, Vikalp.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xi, 66 p. :ill., digital ;24 cm.
內容註:	Aircraft System Identification -- Neural Modeling and Parameter Estimation -- Identification of Aircraft Longitudinal Derivatives -- Identification of Aircraft Lateral-directional Derivatives -- Identification of a Flexible Aircraft Derivatives -- Conclusions and Future Work -- Appendix A: Neural Network Based Solution of Ordinary Differential Equation -- Appendix B: Output Error Method.
Contained By:	Springer Nature eBook
標題:	Aerodynamics - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-981-16-0104-0
ISBN:	9789811601040

Aircraft aerodynamic parameter estimation from flight data using neural partial differentiation
Mohamed, Majeed.

Aircraft aerodynamic parameter estimation from flight data using neural partial differentiation[electronic resource] /by Majeed Mohamed, Vikalp Dongare. - Singapore :Springer Singapore :2021. - xi, 66 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Aircraft System Identification -- Neural Modeling and Parameter Estimation -- Identification of Aircraft Longitudinal Derivatives -- Identification of Aircraft Lateral-directional Derivatives -- Identification of a Flexible Aircraft Derivatives -- Conclusions and Future Work -- Appendix A: Neural Network Based Solution of Ordinary Differential Equation -- Appendix B: Output Error Method.

This book presents neural partial differentiation as an estimation algorithm for extracting aerodynamic derivatives from flight data. It discusses neural modeling of the aircraft system. The neural partial differentiation approach discussed in the book helps estimate parameters with their statistical information from the noisy data. Moreover, this method avoids the need for prior information about the aircraft model parameters. The objective of the book is to extend the use of the neural partial differentiation method to the multi-input multi-output aircraft system for the online estimation of aircraft parameters from an established neural model. This approach will be relevant for the design of an adaptive flight control system. The book also discusses the estimation of aerodynamic derivatives of rigid and flexible aircraft which are treated separately. The longitudinal and lateral-directional derivatives of aircraft are estimated from flight data. Besides the aerodynamic derivatives, mode shape parameters of flexible aircraft are also identified in the book as part of identification for the state space aircraft model. Since the detailed description of the approach is illustrated through the block diagram and their results are presented in tabular form with figures of parameters converge to their estimates, the contents of this book are intended for readers who want to pursue a postgraduate and doctoral degree in science and engineering. This book is useful for practicing scientists, engineers, and teachers in the field of aerospace engineering.

ISBN: 9789811601040

Standard No.: 10.1007/978-981-16-0104-0doiSubjects--Topical Terms:

880544
Aerodynamics
--Mathematical models.

LC Class. No.: TL573

Dewey Class. No.: 629.1323

Aircraft aerodynamic parameter estimation from flight data using neural partial differentiation
LDR:03082nmm a2200349 a 4500 001 2238272
003 DE-He213
005 20210520133417.0
006 m d
007 cr nn 008maaau
008 211111s2021 si s 0 eng d
020 $a 9789811601040 $q (electronic bk.)
020 $a 9789811601033 $q (paper)
024 7 $a 10.1007/978-981-16-0104-0 $2 doi
035 $a 978-981-16-0104-0
040 $a GP $c GP
041 0 $a eng
050 4 $a TL573
072 7 $a TRP $2 bicssc
072 7 $a TEC002000 $2 bisacsh
072 7 $a TRP $2 thema
072 7 $a TTDS $2 thema
082 0 4 $a 629.1323 $2 23
090 $a TL573 $b .M697 2021
100 1 $a Mohamed, Majeed. $3 3491248
245 1 0 $a Aircraft aerodynamic parameter estimation from flight data using neural partial differentiation $h [electronic resource] / $c by Majeed Mohamed, Vikalp Dongare.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xi, 66 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
505 0 $a Aircraft System Identification -- Neural Modeling and Parameter Estimation -- Identification of Aircraft Longitudinal Derivatives -- Identification of Aircraft Lateral-directional Derivatives -- Identification of a Flexible Aircraft Derivatives -- Conclusions and Future Work -- Appendix A: Neural Network Based Solution of Ordinary Differential Equation -- Appendix B: Output Error Method.
520 $a This book presents neural partial differentiation as an estimation algorithm for extracting aerodynamic derivatives from flight data. It discusses neural modeling of the aircraft system. The neural partial differentiation approach discussed in the book helps estimate parameters with their statistical information from the noisy data. Moreover, this method avoids the need for prior information about the aircraft model parameters. The objective of the book is to extend the use of the neural partial differentiation method to the multi-input multi-output aircraft system for the online estimation of aircraft parameters from an established neural model. This approach will be relevant for the design of an adaptive flight control system. The book also discusses the estimation of aerodynamic derivatives of rigid and flexible aircraft which are treated separately. The longitudinal and lateral-directional derivatives of aircraft are estimated from flight data. Besides the aerodynamic derivatives, mode shape parameters of flexible aircraft are also identified in the book as part of identification for the state space aircraft model. Since the detailed description of the approach is illustrated through the block diagram and their results are presented in tabular form with figures of parameters converge to their estimates, the contents of this book are intended for readers who want to pursue a postgraduate and doctoral degree in science and engineering. This book is useful for practicing scientists, engineers, and teachers in the field of aerospace engineering.
650 0 $a Aerodynamics $x Mathematical models. $3 880544
650 1 4 $a Aerospace Technology and Astronautics. $3 928116
650 2 4 $a Automotive Engineering. $3 928032
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 891089
700 1 $a Dongare, Vikalp. $3 3491249
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in applied sciences and technology. $3 1565541
856 4 0 $u https://doi.org/10.1007/978-981-16-0104-0
950 $a Engineering (SpringerNature-11647)