%0 Journal Article
	%A M.V Rajesh and  Archana R and  A Unnikrishnan and  R Gopikakumari and  Jeevamma Jacob
	%D 2008
	%J International Journal of Electronics and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 24, 2008
	%T Evaluation of the ANN Based Nonlinear System Models in the MSE and CRLB Senses
	%U https://publications.waset.org/pdf/9958
	%V 24
	%X The System Identification problem looks for a
suitably parameterized model, representing a given process. The
parameters of the model are adjusted to optimize a performance
function based on error between the given process output and
identified process output. The linear system identification field is
well established with many classical approaches whereas most of
those methods cannot be applied for nonlinear systems. The problem
becomes tougher if the system is completely unknown with only the
output time series is available. It has been reported that the
capability of Artificial Neural Network to approximate all linear and
nonlinear input-output maps makes it predominantly suitable for the
identification of nonlinear systems, where only the output time series
is available. [1][2][4][5]. The work reported here is an attempt to
implement few of the well known algorithms in the context of
modeling of nonlinear systems, and to make a performance
comparison to establish the relative merits and demerits.
	%P 2681 - 2685