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

• Published in: IEEE transactions on signal processing Vol. 42; no. 2; pp. 489 - 491
• Main Authors: Xinhua Zhuang, Yan Huang, Yu, F.A., Peng Zhang
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.02.1994; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Array signal processing; Autocorrelation; Entropy; Exact sciences and technology; Information, signal and communications theory; Multidimensional signal processing; Multidimensional systems; Neural networks; Signal and communications theory; Signal processing algorithms; Signal representation. Spectral analysis; Signal, noise; Spectral analysis; Speech processing; Telecommunications and information theory; Very large scale integration; Differential equation; Initial value problem; Method of maximum entropy; Neural network; Spectral analysis; Lyapunov equation; Constrained optimization
• ISSN: 1053-587X
• DOI: 10.1109/78.275638

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.< >