@inproceedings{aef44f8e0ca74f23b0e8d727edfc1e87,
title = "Volterra-Wiener characterization of a recurrent neural network",
abstract = "The Volterra-Wiener theory of nonlinear systems to neural network modeling is studied. A recursive formula to compute the Volterra kernels of a well-known recurrent neural network is presented. Expressing the neural network input-output relationship in this framework allows issues such as generalization, functional representation power, and optimal choice of the neuron activation function to be addressed. The issue of neural network realization of a subclass of systems admitting Volterra expansion is also addressed. Such an approach is expected to shed some light on these issues and help design neural networks as models for signal processing and nonlinear control.",
author = "Hakim, {N. Z.} and Kaufman, {J. J.} and G. Cerf and Meadows, {H. E.}",
year = "1991",
language = "English",
isbn = "0780302168",
series = "Proceedings of the Annual Conference on Engineering in Medicine and Biology",
publisher = "Publ by IEEE",
number = "pt 3",
pages = "1397--1398",
booktitle = "Proceedings of the Annual Conference on Engineering in Medicine and Biology",
edition = "pt 3",
note = "Proceedings of the 13th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; Conference date: 31-10-1991 Through 03-11-1991",
}