A neural net algorithm for multidimensional minimum relative-entropy spectral analysis

Author

Xinhua Zhuang ; Yan Huang ; Yu, Frank A. ; Zhang, Peng

Author_Institution

Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA

Volume

42

Issue

2

fYear

1994

fDate

2/1/1994 12:00:00 AM

Firstpage

489

Lastpage

491

Abstract

A neural net algorithm is presented to solve the general 1-D or multidimensional minimum relative-entropy spectral analysis. The problem is formulated as a primal constrained optimization and is reduced to solving an initial value problem of differential equation of Lyapunov type. The initial value problem of Lyapunov system comprises the basis of the neural net algorithm. Experiments with simulated data convincingly showed that the algorithm did provide the multidimensional minimum relative-entropy spectral estimator with the autocorrelation matching property with computational efficiency

Keywords

differential equations; initial value problems; neural nets; optimisation; spectral analysis; Lyapunov differential equation; autocorrelation matching; computational efficiency; constrained optimization; initial value problem; multidimensional minimum relative-entropy; neural net algorithm; simulated data; spectral analysis; Array signal processing; Autocorrelation; Entropy; Multidimensional signal processing; Multidimensional systems; Neural networks; Signal processing algorithms; Spectral analysis; Speech processing; Very large scale integration;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.275638

Filename

275638