Fractional fourier transform pre-processing for neural networks and its application to object recognition

Date
2002-01
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Source Title
Neural Networks
Print ISSN
0893-6080
Electronic ISSN
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.

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Keywords
Fractional fourier transform, Neural networks, Input pre-processing, Object recognition, Position estimation, Sonar, Acoustic signal processing
Citation
Published Version (Please cite this version)