Hopfield-Tank neural net: Walsh to Fourier transform

D. A. Karras; S. J. Varoufakis; G. E. Antoniou; G. B. Carayannis

doi:10.1016/0925-2312(92)90042-N

Hopfield-Tank neural net: Walsh to Fourier transform

D. A. Karras, S. J. Varoufakis, G. E. Antoniou, G. B. Carayannis

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

A simple method for the computation of the Discrete Fourier Transform (DFT) from the Walsh Transform, using the Neural Network model of Hopfield and Tank, is presented. The proposed method circumvents the algorithmic complexity of the DFT. The computation time for the evaluation of the DFT coefficients depends upon a time constant which characterizes the neural network.

Original language	English
Pages (from-to)	37-41
Number of pages	5
Journal	Neurocomputing
Volume	4
Issue number	1-2
DOIs	https://doi.org/10.1016/0925-2312(92)90042-N
State	Published - Feb 1992

Keywords

Fourier transform
Hopfield-Tank model
Neural networks
Walsh transform

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10.1016/0925-2312(92)90042-N

Cite this

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title = "Hopfield-Tank neural net: Walsh to Fourier transform",

abstract = "A simple method for the computation of the Discrete Fourier Transform (DFT) from the Walsh Transform, using the Neural Network model of Hopfield and Tank, is presented. The proposed method circumvents the algorithmic complexity of the DFT. The computation time for the evaluation of the DFT coefficients depends upon a time constant which characterizes the neural network.",

keywords = "Fourier transform, Hopfield-Tank model, Neural networks, Walsh transform",

author = "Karras, {D. A.} and Varoufakis, {S. J.} and Antoniou, {G. E.} and Carayannis, {G. B.}",

year = "1992",

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AU - Karras, D. A.

AU - Varoufakis, S. J.

AU - Antoniou, G. E.

AU - Carayannis, G. B.

PY - 1992/2

Y1 - 1992/2

N2 - A simple method for the computation of the Discrete Fourier Transform (DFT) from the Walsh Transform, using the Neural Network model of Hopfield and Tank, is presented. The proposed method circumvents the algorithmic complexity of the DFT. The computation time for the evaluation of the DFT coefficients depends upon a time constant which characterizes the neural network.

AB - A simple method for the computation of the Discrete Fourier Transform (DFT) from the Walsh Transform, using the Neural Network model of Hopfield and Tank, is presented. The proposed method circumvents the algorithmic complexity of the DFT. The computation time for the evaluation of the DFT coefficients depends upon a time constant which characterizes the neural network.

KW - Fourier transform

KW - Hopfield-Tank model

KW - Neural networks

KW - Walsh transform

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DO - 10.1016/0925-2312(92)90042-N

M3 - Article

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VL - 4

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JO - Neurocomputing

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ER -

Hopfield-Tank neural net: Walsh to Fourier transform

Abstract

Keywords

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