Back-propagation network for classifying auditory brainstem evoked potentials: Input level biasing, temporal and spectral inputs and learning patterns

Dogan Alpsan, Ozcan Ozdamar

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

1 Scopus citations

Abstract

Summary form only given, as follows. The results of an investigation conducted to examine the effects of various input data forms on learning of a neural network for classifying auditory evoked potentials are presented. The long-term objective is to use the classification in an automated device for hearing threshold testing. Feedforward multilayered neural networks trained with the backpropagation method are used. The effects of presenting the data to the neural network in various temporal and spectral modes are explored. Results indicate that temporal and spectral information complement one another and increase performance when used together. Learning curves and dot graphs as they are used in this study may reveal network learning strategies. The nature of such learning patterns found in this study is discussed.

Original languageEnglish
Title of host publicationIJCNN Int Jt Conf Neural Network
Editors Anon
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
StatePublished - Dec 1 1989
EventIJCNN International Joint Conference on Neural Networks - Washington, DC, USA
Duration: Jun 18 1989Jun 22 1989

Other

OtherIJCNN International Joint Conference on Neural Networks
CityWashington, DC, USA
Period6/18/896/22/89

ASJC Scopus subject areas

  • Engineering(all)

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