Open AccessImproved efficient proportionate affine projection algorithm based on l 0 ‐norm for sparse system identification
Author(s) -
Zhao Haiquan,
Yu Yi
Publication year - 2014
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2013.0129
Subject(s) - algorithm , norm (philosophy) , identification (biology) , affine transformation , convergence (economics) , computer science , system identification , projection (relational algebra) , mathematical optimization , mathematics , measure (data warehouse) , data mining , botany , political science , pure mathematics , law , economics , biology , economic growth
A new improved memorised improved proportionate affine projection algorithm (IMIPAPA) is proposed to improve the convergence performance of sparse system identification, which incorporates l 0 ‐norm as a measure of sparseness into a recently proposed MIPAPA algorithm. In addition, a simplified implementation of the IMIPAPA (SIMIPAPA) with low‐computational burden is presented while maintaining the consistent convergence performance. The simulation results demonstrate that the IMIPAPA and SIMIPAPA algorithms outperform the MIPAPA algorithm for sparse system identification.