@InProceedings{6952231,
  author = {G. Glentis},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Adaptive identification of sparse systems using the slim approach},
  year = {2014},
  pages = {760-764},
  abstract = {In this paper, a novel time recursive implementation of the Sparse Learning via Iterative Minimization (SLIM) algorithm is proposed, in the context of adaptive system identification. The proposed scheme exhibits fast convergence and tracking ability at an affordable computational cost. Numerical simulations illustrate the achieved performance gain in comparison to other existing adaptive sparse system identification techniques.},
  keywords = {adaptive signal processing;compressed sensing;identification;iterative methods;learning (artificial intelligence);minimisation;SLIM approach;sparse learning via iterative minimization algorithm;time recursive algorithm;numerical simulations;computational cost;fast convergence;tracking ability;adaptive sparse system identification techniques;compressing sensing;Signal processing algorithms;Radio frequency;Adaptive systems;Convergence;Context;Algorithm design and analysis;Signal processing;Adaptive system identification;Sparse systems;SLIM algorithm},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569925467.pdf},
}

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