This correspondence presents analytical results and Monte Carlo simulations
for the fluctuation behavior of a stochastic gradient adaptive identificat
ion scheme. This scheme identifies a polynomial Wiener system (linear FIR f
ilter followed by a static polynomial nonlinearity) for noisy output observ
ations. The analysis includes 1) bounds and a recursion for the misadjustme
nt matrix and 2) algorithm mean square stability regions. A diagonal step-s
ize matrix for the adaptive coefficients is introduced to speed up converge
nce. The theoretical predictions of the fluctuation analysis are supported
bg Monte Carlo simulations.