Title :
Power spectrum estimation via neural-type structured network
Author :
Jun Yin ; Zhaoda Zhu
Author_Institution :
Nanjing Aeronaut. Inst., China
Abstract :
We emphasise the learning algorithm and the convergence capability of the structured network for solving linear equations (Wang et al., 1990). Based on this structured network, a new autoregressive (AR) modeling method is presented. Its basic idea is to solve Yule-Walker type matrix equations for model coefficients by the structured network. The advantages of this AR modeling method over other AR modeling methods are: a parallel architecture and algorithm, suitable for VLSI hardware realization; and no divisions are involved in the calculations, so that the method still works for ill-conditioned Yule-Walker type matrix equations. Simulation results illustrating the performance of the method are given for both narrow-band sources and combinations of narrow-band and broad-band sources subjected to various levels of Gaussian white noise.<>
Keywords :
backpropagation; matrix algebra; neural nets; parallel algorithms; random noise; stochastic processes; time series; Gaussian white noise; VLSI hardware; Yule-Walker matrix equations; autoregressive modeling method; broad-band sources; convergence capability; ill-conditioned Yule-Walker equations; learning algorithm; linear equations; narrow-band sources; neural structured network; parallel algorithm; parallel architecture; power spectrum estimation; Computer networks; Covariance matrix; Eigenvalues and eigenfunctions; Equations; Matrices; Narrowband; Neural networks; Parallel architectures; Spectral analysis; Very large scale integration;
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
DOI :
10.1109/TENCON.1993.320145
