Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Adaptive Neural Network Based Approach for Active Flow Control
Date
2003-01-01
Author
Idan, Moshe
Calise, Anthony J
Kutay, Ali Türker
David E, Parekh
Metadata
Show full item record
Item Usage Stats
165
views
0
downloads
Cite This
URI
http://www.springer.com/us/book/9783211007532
https://hdl.handle.net/11511/84197
Relation
Manipulation and Control of Jets in Crossflow
Collections
Department of Aerospace Engineering, Book / Book chapter
Suggestions
OpenMETU
Core
Adaptive Neural Network Applications on Missile Controller Design
Yavrucuk, İlkay (2009-08-17)
Adaptive neural network applications on missile controller design
Sağıroğlu, Serkan; Yavrucuk, İlkay; Department of Aerospace Engineering (2009)
In this thesis, adaptive neural network controllers are designed for a high subsonic cruise missile. Two autopilot designs are included in the study using adaptive neural networks, namely an altitude hold autopilot designed for the longitudinal channel and a directional autopilot designed for heading control. Aerodynamic coefficients are obtained using missile geometry; a 5-Degree of Freedom (5-DOF) simulation model is obtained, and linearized at a single trim condition. An inverted model is used in the con...
Adaptive decentralized control of interconnected systems
Sezer, ME; Altunel, H (2004-08-01)
This paper presents a decentralized adaptive stabilization scheme for a class of interconnected systems using high-gain adaptive controllers. The nominal subsystems are assumed to satisfy some mild conditions required by standard adaptive control schemes, and the interconnections certain structural conditions. The decentralized controllers are high-gain dynamic systems operating on local outputs to generate local control inputs. Both continuous-time and sampled-data controllers are considered. The idea behi...
Adaptive estimation and hypothesis testing methods
Dönmez, Ayça; Tiku, Moti Lal; Department of Statistics (2010)
For statistical estimation of population parameters, Fisher’s maximum likelihood estimators (MLEs) are commonly used. They are consistent, unbiased and efficient, at any rate for large n. In most situations, however, MLEs are elusive because of computational difficulties. To alleviate these difficulties, Tiku’s modified maximum likelihood estimators (MMLEs) are used. They are explicit functions of sample observations and easy to compute. They are asymptotically equivalent to MLEs and, for small n, are equal...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. Idan, A. J. Calise, A. T. Kutay, and P. David E,
Adaptive Neural Network Based Approach for Active Flow Control
. 2003, p. 297.