Tracking of sinusoidal frequencies by neural network learning algorithms

Author

Karhunen, Juha ; Joutsensalo, Jyrki

Author_Institution

Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland

fYear

1991

fDate

14-17 Apr 1991

Firstpage

3177

Abstract

An adaptive signal subspace estimation algorithm with a natural interpretation in terms of neural network concepts is considered. This algorithm contains only relatively simple operations and has self-orthornormalizing properties. It is demonstrated that the algorithm can learn and track the frequency information in an unsupervised manner from the data samples. After convergence, the connection weights of the network directly define a frequency estimator. Practical issues and some related algorithms are discussed

Keywords

computerised signal processing; learning systems; neural nets; parameter estimation; tracking; adaptive signal subspace estimation algorithm; neural network learning algorithms; self-orthornormalizing properties; sinusoidal frequency estimation; sinusoidal frequency tracking; Artificial neural networks; Autocorrelation; Computer networks; Convergence; Fault tolerance; Frequency estimation; Laboratories; Neural networks; Parallel processing; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Conference_Location

Toronto, Ont.

ISSN

1520-6149

Print_ISBN

0-7803-0003-3

Type

conf

DOI

10.1109/ICASSP.1991.150130

Filename

150130