TY - GEN
T1 - Frequency domain identification of hammerstein systems
AU - Swain, Akshya K.
AU - Westwick, David T.
AU - Perreault, Eric
PY - 2009/12/1
Y1 - 2009/12/1
N2 - The present study proposes a new approach to identify the parameters of both continuous and discrete time Hammerstein systems in frequency domain. A harmonic probing technique is used to derive the linear and higher-order frequency response functions (called the generalized frequency response functions (GFRF)) of both discrete and continuous-time Hammerstein models. The computation of the n-th order generalized frequency response functions (GFRF) is a recursive procedure where each lower order GFRF contains no effects from higher order terms. Thus the parameter estimation problem can be formulated in a linear least squares framework where the parameters corresponding to nonlinearities of different orders can be estimated independently, beginning with first order and then building up to include the nonlinear terms using the weighted complex orthogonal estimator, which is a modified version of the standard orthogonal least squares, to accommodate complex data. Simulation results are included to demonstrate that the proposed method can successfully estimate the parameters of the system under the effects of significant levels of noise.
AB - The present study proposes a new approach to identify the parameters of both continuous and discrete time Hammerstein systems in frequency domain. A harmonic probing technique is used to derive the linear and higher-order frequency response functions (called the generalized frequency response functions (GFRF)) of both discrete and continuous-time Hammerstein models. The computation of the n-th order generalized frequency response functions (GFRF) is a recursive procedure where each lower order GFRF contains no effects from higher order terms. Thus the parameter estimation problem can be formulated in a linear least squares framework where the parameters corresponding to nonlinearities of different orders can be estimated independently, beginning with first order and then building up to include the nonlinear terms using the weighted complex orthogonal estimator, which is a modified version of the standard orthogonal least squares, to accommodate complex data. Simulation results are included to demonstrate that the proposed method can successfully estimate the parameters of the system under the effects of significant levels of noise.
UR - http://www.scopus.com/inward/record.url?scp=77951117523&partnerID=8YFLogxK
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U2 - 10.1109/TENCON.2009.5395973
DO - 10.1109/TENCON.2009.5395973
M3 - Conference contribution
AN - SCOPUS:77951117523
SN - 9781424445479
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
BT - TENCON 2009 - 2009 IEEE Region 10 Conference
T2 - 2009 IEEE Region 10 Conference, TENCON 2009
Y2 - 23 November 2009 through 26 November 2009
ER -