A Variable Step Size Adaptive Algorithm With Simple Parameter Selection
Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
Abstract
We propose a normalized least mean squares algo- rithm with variable step size. Unlike other solutions, it has low computational cost, only three parameters that are simple to choose, and its steady-state performance can be easily predicted. Simulations show a competitive performance in comparison with other solutions, and validate our theoretical analysis.
Topics & Concepts
Simple (philosophy)AlgorithmVariable (mathematics)Computer scienceMathematical optimizationSelection (genetic algorithm)MathematicsArtificial intelligenceEpistemologyMathematical analysisPhilosophyAdvanced Adaptive Filtering TechniquesBlind Source Separation TechniquesControl Systems and Identification