Low sensitivity time delay neural networks with cascade form structure

Andrew D. Back, Bill G. Horne, Ah Chung Tsoi, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In current practice, tapped delay line models such as the time delay neural network (TDNN) are commonly implemented using a direct form structure. In this paper, we show that the problem of high parameter sensitivity, well known in linear systems, also applies to nonlinear models such as the TDNN. To overcome the consequent numerical problems, we propose a cascade form TDNN (CTDNN) and show its advantages over the commonly used direct form TDNN.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
PublisherIEEE
Pages44-53
Number of pages10
StatePublished - 1997
EventProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97 - Amelia Island, FL, USA
Duration: Sep 24 1997Sep 26 1997

Other

OtherProceedings of the 1997 7th IEEE Workshop on Neural Networks for Signal Processing, NNSP'97
CityAmelia Island, FL, USA
Period9/24/979/26/97

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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