Name
Abstract | ||
|---|---|---|
This work proposes a low complexity nonlinearity model and develops adaptive algorithms over it. The model is based on the decomposable---or rank-one, in tensor language---Volterra kernels. It may also be described as a product of FIR filters, which explains its low-complexity. The rank-one model is also interesting because it comes from a well-posed problem in approximation theory. The paper uses such model in an estimation theory context to develop an exact gradient-type algorithm, from which adaptive algorithms such as the least mean squares (LMS) filter and its data-reuse version---the TRUE-LMS---are derived. Stability and convergence issues are addressed. The algorithms are then tested in simulations, which show its good performance when compared to other nonlinear processing algorithms in the literature. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Systems and Control | Least mean squares filter,Convergence (routing),Mathematical optimization,Nonlinear system,Tensor,Approximation theory,Algorithm,Probabilistic analysis of algorithms,Adaptive filter,Mathematics,Recursive least squares filter |
DocType | Volume | Citations |
Journal | abs/1610.07520 | 1 |
PageRank | References | Authors |
0.36 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felipe C. Pinheiro | 1 | 1 | 1.03 |
| Cássio Guimarães Lopes | 2 | 394 | 32.32 |