Proc. SPIE. 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012)
KEYWORDS: Nonlinear filtering, Error analysis, Simulation of CCA and DLA aggregates, Digital filtering, Computer simulations, Performance modeling, Electronic filtering, Amplifiers, Nonlinear optics, Condition numbers
An regularization approach is introduced into the online identification of inverse model for predistortion. It is based
on a modified backpropagation Levenberg-Marquardt algorithm with sliding window. Adaptive predistorter with
feedback was identified respectively based on direct learning and indirect learning architectures. Length of the sliding
window was discussed. Compared with the Recursive Prediction Error Method (RPEM) algorithm and Nonlinear
Filtered Least-Mean-Square (NFxLMS) algorithm, the algorithm is tested by identification of infinite impulse response
Wiener predistorter. It is found that the proposed algorithm is much more efficient than either of the other techniques.
The values of the parameters are also smaller than those extracted by the ordinary least-squares algorithm since the
proposed algorithm constrains the L2-norm of the parameters.