A modified Hebbian learning rule for total least-squares estimation with complex-valued arguments

Author

Gao, Keqin ; Ahmad, M. Omair ; Swamy, M.N.S.

Author_Institution

Dept. of Electr. & Comput. Eng., Concordia Univ., Montreal, Que., Canada

Volume

3

fYear

1992

fDate

10-13 May 1992

Firstpage

1231

Abstract

A constrained anti-Hebbian algorithm that is used for processing complex signals is presented. It is shown that the algorithm adaptively extracts the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal. The operation of the algorithm is simple, similar to that of the LMS (least mean square) algorithm, and it can be applied to an adaptive prediction-error filter directly, giving an estimate of the parameters that is optimal in the total least-squares sense. Simulation results on estimating the frequencies of sinusoids corrupted by white noise are presented

Keywords

Hebbian learning; adaptive filters; eigenvalues and eigenfunctions; filtering and prediction theory; least squares approximations; signal processing; Hebbian learning rule; adaptive prediction-error filter; complex signal processing; constrained antiHebbian algorithm; eigenvalue; eigenvector; input signal correlation matrix; neural nets; sinusoid frequencies; total least-squares estimation; white noise; Adaptive filters; Algorithm design and analysis; Filtering algorithms; Finite impulse response filter; Hebbian theory; Neurons; Parameter estimation; Signal processing; Signal processing algorithms; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.230302

Filename

230302