Table of Contents
Advances in Artificial Neural Systems
Volume 2010 (2010), Article ID 984381, 11 pages
http://dx.doi.org/10.1155/2010/984381
Research Article

Determination of Complex-Valued Parametric Model Coefficients Using Artificial Neural Network Technique

Department of Mechatronics, International Islamic University Malaysia (IIUM), P.O. Box 10, 53100 Gombak, Malaysia

Received 10 April 2009; Accepted 8 March 2010

Academic Editor: Yehoshua Zeevi

Copyright © 2010 A. M. Aibinu 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.

Abstract

A new approach for determining the coefficients of a complex-valued autoregressive (CAR) and complex-valued autoregressive moving average (CARMA) model coefficients using complex-valued neural network (CVNN) technique is discussed in this paper. The CAR and complex-valued moving average (CMA) coefficients which constitute a CARMA model are computed simultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. The performance of the proposed technique has been evaluated using simulated complex-valued data (CVD) with three different types of activation functions. The results show that the proposed method can accurately determine the model coefficients provided that the network is properly trained. Furthermore, application of the developed CVNN-based technique for MRI K-space reconstruction results in images with improve resolution.