@article{(Open Science Index):https://publications.waset.org/pdf/1857,
	  title     = {Variable Step-Size Affine Projection Algorithm With a Weighted and Regularized Projection Matrix},
	  author    = {Tao Dai and  Andy Adler and  Behnam Shahrrava},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents a forgetting factor scheme for variable step-size affine projection algorithms (APA). The proposed scheme uses a forgetting processed input matrix as the projection matrix of pseudo-inverse to estimate system deviation. This method introduces temporal weights into the projection matrix, which is typically a better model of the real error's behavior than homogeneous temporal weights. The regularization overcomes the ill-conditioning introduced by both the forgetting process and the increasing size of the input matrix. This algorithm is tested by independent trials with coloured input signals and various parameter combinations. Results show that the proposed algorithm is superior in terms of convergence rate and misadjustment compared to existing algorithms. As a special case, a variable step size NLMS with forgetting factor is also presented in this paper.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {2},
	  year      = {2008},
	  pages     = {338 - 345},
	  ee        = {https://publications.waset.org/pdf/1857},
	  url   	= {https://publications.waset.org/vol/14},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 14, 2008},
	}