| Tag |
First Indicator |
Second Indicator |
Subfields |
| LEADER |
00000cam a2200000Mi 4500 |
| 001 |
in00003579839 |
| 006 |
m o d |
| 007 |
cr mnu---uuaaa |
| 008 |
130109s2002 mau o 000 0 eng |
| 005 |
20260420213247.2 |
| 020 |
|
|
|a 9781475752861 (electronic bk.)
|
| 020 |
|
|
|a 1475752865 (electronic bk.)
|
| 020 |
|
|
|z 9781441949158
|
| 020 |
|
|
|z 1441949151
|
| 035 |
|
|
|a (OCoLC)851747062
|
| 040 |
|
|
|a AU@
|b eng
|c AU@
|d OCLCO
|d OCLCQ
|d GW5XE
|d OCLCF
|d UtOrBLW
|
| 049 |
|
|
|a TXAM
|
| 050 |
|
4 |
|a QC174.7-175.36
|
| 072 |
|
7 |
|a PHS
|2 bicssc
|
| 072 |
|
7 |
|a PHDT
|2 bicssc
|
| 072 |
|
7 |
|a SCI055000
|2 bisacsh
|
| 082 |
0 |
4 |
|a 621
|2 23
|
| 100 |
1 |
|
|a Sundararajan, N.
|
| 245 |
1 |
0 |
|a Fully Tuned Radial Basis Function Neural Networks for Flight Control /
|c by N. Sundararajan, P. Saratchandran, Yan Li.
|
| 264 |
|
1 |
|a Boston, MA :
|b Springer US,
|c 2002.
|
| 300 |
|
|
|a 1 online resource (xv, 158 pages)
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 490 |
1 |
|
|a The Springer International Series on Asian Studies in Computer and Information Science,
|x 1566-0710 ;
|v 12
|
| 520 |
|
|
|a Fully Tuned Radial Basis Function Neural Networks for Flight Control presents the use of the Radial Basis Function (RBF) neural networks for adaptive control of nonlinear systems with emphasis on flight control applications. A Lyapunov synthesis approach is used to derive the tuning rules for the RBF controller parameters in order to guarantee the stability of the closed loop system. Unlike previous methods that tune only the weights of the RBF network, this book presents the derivation of the tuning law for tuning the centers, widths, and weights of the RBF network, and compares the results with existing algorithms. It also includes a detailed review of system identification, including indirect and direct adaptive control of nonlinear systems using neural networks. Fully Tuned Radial Basis Function Neural Networks for Flight Control is an excellent resource for professionals using neural adaptive controllers for flight control applications.
|
| 500 |
|
|
|a Electronic resource.
|
| 650 |
|
0 |
|a Physics.
|
| 650 |
|
0 |
|a Artificial intelligence.
|
| 650 |
|
0 |
|a Mathematical optimization.
|
| 650 |
|
0 |
|a Engineering.
|
| 650 |
|
7 |
|a Artificial intelligence.
|2 fast
|0 (OCoLC)fst00817247
|
| 650 |
|
7 |
|a Engineering.
|2 fast
|0 (OCoLC)fst00910312
|
| 650 |
|
7 |
|a Mathematical optimization.
|2 fast
|0 (OCoLC)fst01012099
|
| 650 |
|
7 |
|a Physics.
|2 fast
|0 (OCoLC)fst01063025
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
|
|a Saratchandran, P.
|
| 700 |
1 |
|
|a Li, Yan.
|
| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 776 |
1 |
8 |
|i Print version:
|z 9781441949158
|
| 830 |
|
0 |
|a Springer International Series on Asian Studies in Computer and Information Science ;
|v 12.
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://link.springer.com/10.1007/978-1-4757-5286-1
|z Connect to the full text of this electronic book
|t 0
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
|
|
|a MARS
|
| 999 |
f |
f |
|s 0f6ae1df-cfec-3a33-a484-83f739c555a4
|i 2b147d47-59e7-3c54-9198-37374b42f3ff
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|e QC174.7-175.36
|h Library of Congress classification
|
| 998 |
f |
f |
|a QC174.7-175.36
|t 0
|l Available Online
|