Open AccessA REDUCED PARAMETER SECOND-ORDER VOLTERRA FILTER WITH APPLICATION TO NONLINEAR ADAPTIVE PREDICTION OF CHAOTIC TIME SERIES
Author(s) -
Jiashu Zhang,
Xin Xiao
Publication year - 2001
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.50.1248
Subject(s) - nonlinear system , chaotic , series (stratigraphy) , volterra series , adaptive filter , filter (signal processing) , least mean squares filter , control theory (sociology) , computer science , mathematics , algorithm , physics , artificial intelligence , paleontology , control (management) , quantum mechanics , computer vision , biology
A reduced parameter second-order Volterra filter (RPSOVF) which is constructed by the multiplication-coupled two linear FIR filters, and its nonlinear normalized least mean square (NLMS) algorithm is proposed; and this RPSOVF with nonlinear NLMS algorithm are used to make adaptive predictions of chaotic time series. The rule of selecting convergent assistant parameters of the nonlinear NLMS algorithm is obtained. Experimental results show that this reduced parameter second-order Volterra filter with the nonlinear NLMS algorithm can be successfully used to make adaptive predictions of chaotic time series, and the modified nonlinear NLMS algorithm enables RPSOVF to converge and stabilize.