Title :
Analysis of stochastic gradient identification of polynomial nonlinear systems with memory
Author :
Celka, P. ; Bershad, N.J. ; Vesin, J.M.
Author_Institution :
Dept. of Electr. Eng., Fed. Inst. of Technol., Zurich, Switzerland
Abstract :
This paper presents analytical, numerical and experimental results for a stochastic gradient adaptive scheme which identifies a polynomial-type nonlinear system with memory for noisy output observations. The analysis includes the computation of the stationary points, the mean square error surface, and the mean behaviour of the algorithm for Gaussian data. Monte Carlo simulations confirm the theoretical predictions which show a small sensitivity to the observation noise
Keywords :
Monte Carlo methods; Wiener filters; adaptive filters; gradient methods; identification; least mean squares methods; nonlinear systems; polynomials; stochastic processes; Gaussian data; Monte Carlo simulations; adaptive scheme; mean behaviour; mean square error surface; memory; noisy output observations; observation noise; polynomial nonlinear systems; stationary points; stochastic gradient identification; Algorithm design and analysis; Least squares approximation; Mean square error methods; Nonlinear filters; Nonlinear systems; Polynomials; Signal analysis; Signal processing algorithms; Signal to noise ratio; Stochastic systems;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5041-3
DOI :
10.1109/ICASSP.1999.756216
