Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing



+44 (0)20 7594 6271d.mandic Website




Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267




813Electrical EngineeringSouth Kensington Campus






BibTex format

author = {Mandic, DP and Chambers, JA},
journal = {IEEE Transactions on Signal Processing},
pages = {1779--1780},
title = {Towardds a PRNN-based a posteriori nonlinear predictor},
volume = {46},
year = {1998}

RIS format (EndNote, RefMan)

AB - For prediction of nonlinear and nonstationary signals, as well as in nonlinear system identification, artificial neural network (ANN) based techniques have been found to be particularly attractive. The pipelined recurrent neural network (PRNN) based nonlinear predictor is an important example of the ANN approach. Here, we address the way the PRNN calculates its weights with respect to the particular time instant at which the signal is available within the network and show that performance of the PRNN-based nonlinear predictor for a given architecture and corresponding learning algorithm can be significantly improved by a careful time-management policy. The concept of an a posteriori PRNNbased nonlinear predictor is introduced, and the algorithms for obtaining such an improved prediction scheme are provided. ©1993 IEEE.
AU - Mandic,DP
AU - Chambers,JA
EP - 1780
PY - 1998///
SN - 1053-587X
SP - 1779
TI - Towardds a PRNN-based a posteriori nonlinear predictor
T2 - IEEE Transactions on Signal Processing
VL - 46
ER -