Rahrooh, A., & Buchanan, W. W., & Seker, R. (2013, June), Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm  Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19884

\bibitem{asee_peer_19884}
  Rahrooh, A., \& Buchanan, W. W., \& Seker, R. (2013, June), \emph{Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm}
  Paper presented at 2013 ASEE Annual Conference \& Exposition, Atlanta, Georgia.
  10.18260/1-2--19884

Alireza Rahrooh, Walter W. Buchanan P.E., and Remzi Seker.  "Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm".  2013 ASEE Annual Conference & Exposition, Atlanta, Georgia, 2013, June.  ASEE Conferences, 2013.  https://peer.asee.org/19884 Internet.  12 Jan, 2025

\bibitem{asee_peer_19884}
  Alireza Rahrooh, Walter W. Buchanan P.E., and Remzi Seker.
  "Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm".
  \emph{2013 ASEE Annual Conference \& Exposition, Atlanta, Georgia, 2013, June}.
  ASEE Conferences, 2013.
  https://peer.asee.org/19884 Internet.  12 Jan, 2025

@INPROCEEDINGS{asee_peer_19884
author = "Alireza Rahrooh, Walter W. Buchanan P.E., and Remzi Seker"
title = "Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm"
booktitle = "2013 ASEE Annual Conference \& Exposition"
year = "2013"
month = "June"
address = "Atlanta, Georgia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/19884}
number = {10.18260/1-2--19884}
}

TY  - CPAPER
AB  -                  Linear Model Estimation of Nonlinear Systems                           Using Least-Squares AlgorithmAbstractThis paper presents a new approach to obtain more accurate linear models of nonlinearsystems using parameter estimation. In particular, a technique is developed to generate anoptimal linear model which is valid over a wide range of trajectories and converges to thedesired steady-state value with no modification of the input using Least-Squares Algorithm.This approach is very efficient and does not require storing the data. Therefore, it can easilybe used and implemented with limited resources for undergraduate curriculum especially inunderdeveloped countries. Most available techniques for linearization of nonlinear systemare only valid about the operating point using “offset derivative”. This technique makes useof the method of finite difference to approximate partial derivatives of a Taylor seriesexpansion of the nonlinear state equations about a nominal operating point or trajectories.The advantages of proposed technique over others are illustrated via obtaining the linearmodels of jet engines nonlinear models.
AU  - Alireza Rahrooh
AU  - Walter W. Buchanan P.E.
AU  - Remzi Seker
CY  - Atlanta, Georgia
DA  - 2013/06/23
PB  - ASEE Conferences
TI  - Linear Model Estimation of Nonlinear Systems Using Least-Squares Algorithm
UR  - https://peer.asee.org/19884
DO  - 10.18260/1-2--19884
ER  -