Nonlinear adaptive prediction using a complex-valued PRNN

A computationally efficient architecture for nonlinear adaptive prediction of complex-valued nonlinear and non-stationary signals is presented. The adaptive predictor is based upon a complex-valued pipelined recurrent neural network (CPRNN) trained by the complex-valued real-time recurrent learning (CRTRL) algorithm. A variable forgetting factor (VFF) is introduced to improve the performance of CPRNN in the non-stationary environment. The analysis is undertaken with respect to the number of the nested modules, forgetting factor, and input memory of the CPRNN. Simulations on real and synthetic complex data support the proposed architecture and algorithms.