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      • Department of Electrical and Electronics Engineering
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      Fractional fourier transform pre-processing for neural networks and its application to object recognition

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      Author(s)
      Barshan, Billur
      Ayrulu, Birsel
      Date
      2002-01
      Source Title
      Neural Networks
      Print ISSN
      0893-6080
      Publisher
      Elsevier
      Volume
      15
      Issue
      1
      Pages
      131 - 140
      Language
      English
      Type
      Article
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      Abstract
      This study investigates fractional Fourier transform pre-processing of input signals to neural networks. The fractional Fourier transform is a generalization of the ordinary Fourier transform with an order parameter a. Judicious choice of this parameter can lead to overall improvement of the neural network performance. As an illustrative example, we consider recognition and position estimation of different types of objects based on their sonar returns. Raw amplitude and time-of-flight patterns acquired from a real sonar system are processed, demonstrating reduced error in both recognition and position estimation of objects. (C) 2002 Elsevier Science Ltd. All rights reserved.
      Keywords
      Fractional fourier transform
      Neural networks
      Input pre-processing
      Object recognition
      Position estimation
      Sonar
      Acoustic signal processing
      Permalink
      http://hdl.handle.net/11693/11173
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/S0893-6080(01)00120-4
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      • Department of Electrical and Electronics Engineering 3863
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