By Topic

Multi-Objective Optimization of NARX Model for System Identification Using Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Loghmanian, S.M.R. ; Fac. of Mech. Eng., Univ. Teknol. Malaysia, Malaysia ; Ahmad, R. ; Jamaluddin, H.

The problem of constructing an adequate and parsimonious nonlinear autoregressive model process with eXogenous input (NARX) structure for modeling nonlinear dynamic system is studied. NARX has been shown to perform function approximation and represent dynamic systems. The structures are usually guessed or selected in accordance with the designer prior knowledge, however the multiplicity of the model parameters make it troublesome to get an optimum structure. The trial and error approach is not efficient and may not arrive to an optimum structure. An alternative algorithm based on multiobjective optimization algorithm is proposed. The developed model should fulfill two criteria or objectives namely good predictive accuracy and optimum model structure. The result shows that the proposed algorithm is able to correctly identify the simulated examples and adequately model real data structure and based on a set of solutions called the Pareto optimal set, from which the best network is selected.

Published in:

Computational Intelligence, Communication Systems and Networks, 2009. CICSYN '09. First International Conference on

Date of Conference:

23-25 July 2009