Differentiating Sonar Reflections from Corners and Planes by Employing an Intelligent Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Signal Processing
The fractional Fourier domain decomposition
Signal Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Optimal filtering in fractional Fourier domains
IEEE Transactions on Signal Processing
The discrete fractional Fourier transform
IEEE Transactions on Signal Processing
Digital computation of the fractional Fourier transform
IEEE Transactions on Signal Processing
Angular decompositions for the discrete fractional signal transforms
Signal Processing
Fractional transforms in optical information processing
EURASIP Journal on Applied Signal Processing
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
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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.