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

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 thispaper. The CAR and complex-valued moving average (CMA) coefficients which constitute a CARMA model are computedsimultaneously from the adaptive weights and coefficients of the linear activation functions in a two-layered CVNN. Theperformance of the proposed technique has been evaluated using simulated complex-valued data (CVD) with three di fferenttypes of activation functions. The results show that the proposed method can accurately determine the model coe fficientsprovided 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.