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Towardds a PRNN-based a posteriori nonlinear predictor

Mandic, DP and Chambers, JA (1998) Towardds a PRNN-based a posteriori nonlinear predictor IEEE Transactions on Signal Processing, 46 (6). pp. 1779-1780.

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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.

Item Type: Article
Divisions : Surrey research (other units)
Authors :
Mandic, DP
Date : 1 December 1998
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:27
Last Modified : 24 Jan 2020 23:58

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