Title :
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
fDate :
2/1/1994 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on
