Determination of complex-valued parametric model coefficients using artificial neural network technique

  • Authors:

  • A. M. Aibinu;M. J. E. Salami;A. A. Shafie

  • Affiliations:

  • Department of Mechatronics, International Islamic University Malaysia, Gombak, Malaysia;Department of Mechatronics, International Islamic University Malaysia, Gombak, Malaysia;Department of Mechatronics, International Islamic University Malaysia, Gombak, Malaysia

  • Venue:
  • Advances in Artificial Neural Systems
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

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 Kspace reconstruction results in images with improve resolution.