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IADIS International Journal on Computer Science and Information Systems

Published by IADIS (International Association for Development of the Information Society) • ISSN (Online): 1646-3692 • ISSN (Print): 1646-3692
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Acceleration of the gradient-type methods

in blind source separation1 David Blanco *
Diego P. Ruiz *
María C. Carrion *
Carlos García- Puntonet+ *
* *Department of Applied Physics, University of Granada, Spain +Department of Computer Architecture and Computer Technology , University of Granada, Spain (Portugal)
* *Department of Applied Physics, University of Granada, Spain +Department of Computer Architecture and Computer Technology , University of Granada, Spain (Portugal)
* *Department of Applied Physics, University of Granada, Spain +Department of Computer Architecture and Computer Technology , University of Granada, Spain (Portugal)
* *Department of Applied Physics, University of Granada, Spain +Department of Computer Architecture and Computer Technology , University of Granada, Spain (Portugal)
Published: June 15, 2009
Pages: 21–29

Abstract

The steepest descendent is the non-linear optimiza-tion method most used in ICA algorithms. The method is used to fi nd the unmixing matrix, which solves the problem and is a minimum of a non-linear cost function. In this paper the use of quasi-Newton optimization methods, instead of gradient-type ICA methods, is studied. These methods can increase the speed of the method what it is corroborated by simulations.

Keywords

radar target recognition natural resonances principal component analysis neural network matrix pencil method.
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Declarations & Ethics

Funding: This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest: The authors declare no competing financial or institutional interests.
Peer Review: Double-blind peer reviewed by international subject specialists.
License: Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
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Blanco, et al. (2009). Acceleration of the gradient-type methods. IADIS International Journal on Computer Science and Information Systems, 4(1). https://doi.org/10.33965/ijcsis_2009_v4i1_03
Blanco, et al. "Acceleration of the gradient-type methods." IADIS International Journal on Computer Science and Information Systems, vol. 4, no. 1, 2009. https://doi.org/10.33965/ijcsis_2009_v4i1_03
Blanco, et al. "Acceleration of the gradient-type methods." IADIS International Journal on Computer Science and Information Systems 4, no. 1 (2009). https://doi.org/10.33965/ijcsis_2009_v4i1_03
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