LEADER 01722nam  2200397z- 450 
001 9910346952603321
005 20231214133128.0
010   $a1000083492
035   $a(CKB)4920000000100996
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/58556
035   $a(EXLCZ)994920000000100996
100   $a20202102d2018      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aRobust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation
210   $cKIT Scientific Publishing$d2018
215   $a1 electronic resource (XXIV, 196 p. p.)
225 1 $aKarlsruher Schriftenreihe Fahrzeugsystemtechnik / Institut für Fahrzeugsystemtechnik
311   $a3-7315-0807-9 
330   $aThis work provides novel robust and regularized algorithms for parameter estimation with applications in vehicle tractive force prediction and mass estimation. Given a large record of real world data from test runs on public roads, recursive algorithms adjusted the unknown vehicle parameters under a broad variation of statistical assumptions for two linear gray-box models.
610   $aTotal Least Squares
610   $aerrors-in-variables
610   $arobust estimation
610   $aOnlinefilter
610   $arecursive estimation
610   $aRobuste Schätzer
610   $aSystemidentifikation
610   $asystem identification
700   $aRhode$b Stephan$4auth$01326685
906   $aBOOK
912   $a9910346952603321
996   $aRobust and Regularized Algorithms for Vehicle Tractive Force Prediction and Mass Estimation$93037663
997   $aUNINA