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Advances in Artificial Intelligence
Volume 2015 (2015), Article ID 184318, 10 pages
Research Article

Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

1Department of Computer Science, Kirkuk University, Kirkuk, Iraq
2Department of Electrical and Electronic Engineering, Universiti Putra Malaysia (UPM), Serdang, Selangor 43300, Malaysia

Received 6 May 2015; Revised 29 July 2015; Accepted 31 August 2015

Academic Editor: Jun He

Copyright © 2015 Dhiadeen Mohammed Salih et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.