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Conference Paper (published)

A new neural network structure for temporal signal processing

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Citation

Hussain A (1997) A new neural network structure for temporal signal processing. In: 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997. ICASSP-97, Volume 4. IEEE International Conferences. 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997. ICASSP-97, Munich, Germany, 21.04.1997-24.04.1997. Piscataway, NJ: IEEE, pp. 3341-3344. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=595509&abstractAccess=no&userType=inst; https://doi.org/10.1109/ICASSP.1997.595509

Abstract
A new two-layer linear-in-the-parameters feedforward network termed the functionally expanded neural network (FENN) is presented, together with its design strategy and learning algorithm. It is essentially a hybrid neural network incorporating a variety of non-linear basis functions within its single hidden layer which emulate other universal approximators employed in the conventional multi-layered perceptron (MLP), radial basis function (RBF) and Volterra neural networks (VNN). The FENN's output error surface is shown to be uni-modal allowing high speed single run learning. A simple strategy based on an iterative pruning retraining scheme coupled with statistical model validation tests is proposed for pruning the FENN. Both simulated chaotic (Mackey-Glass time series) and real-world noisy, highly nonstationary (sunspot) time series are used to illustrate the superior modeling and prediction performance of the FENN compared with other previously reported, more complex neural network based predictor models.

StatusPublished
Title of seriesIEEE International Conferences
Publication date31/12/1997
Publication date online30/04/1997
PublisherIEEE
Publisher URL
Place of publicationPiscataway, NJ
ISSN of series1520-6149
ISBN0-8186-7919-0
Conference1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997. ICASSP-97
Conference locationMunich, Germany
Dates