A Class of Diffusion Algorithms with Logarithmic Cost over Adaptive Sparse Volterra Network
This work provides an incremental improvement in adaptive filtering for sparse nonlinear system identification, particularly in impulsive noise environments.
The authors propose a new class of diffusion algorithms for estimating coefficients of sparse Volterra networks, using logarithmic cost and l0-norm constraint. Simulations show superior performance over existing algorithms in both Gaussian and impulsive noise scenarios.
In this Letter, we present a novel class of diffusion algorithms that can be used to estimate the coefficients of sparse Volterra network (SVN). The development of the algorithms is based on the logarithmic cost and l0-norm constraint. Simulations for Gaussian and impulsive scenarios are conducted to demonstrate the superior performance of the proposed algorithms as compared with the existing algorithms.