The Volterra and Wiener Theories of Nonlinear Systems
The Volterra and Wiener Theories of Nonlinear Systems
A sparse-interpolated scheme for implementing adaptive volterra filters
IEEE Transactions on Signal Processing
Efficient NLMS and RLS algorithms for perfect and imperfect periodic sequences
IEEE Transactions on Signal Processing
A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers
IEEE Transactions on Signal Processing
Random and pseudorandom inputs for Volterra filter identification
IEEE Transactions on Signal Processing
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This paper introduces computationally efficient NLMS and RLS adaptive algorithms for identifying non-recursive, linear-in-the-parameters (LIP) nonlinear systems using periodic input sequences. The algorithms presented in the paper are exact and require a real-time computational effort of a single multiplication, an addition and a subtraction per input sample. The transient, steady state, and tracking behavior of the algorithms as well as the effect of model mismatch is studied in the paper. The low computational complexity, good performance and broad applicability make the approach of this paper a valuable alternative to the current techniques for nonlinear system identification.