A stochastic analysis of the affine projection algorithm for Gaussian autoregressive inputs
نویسندگان
چکیده
This paper studies the statistical behavior of the Affine Projection (AP) algorithm for μ = 1 for Gaussian Autoregressive inputs. This work extends the theoretical results of Rupp [3] to the numerical evaluation of the MSE learning curves for the adaptive AP weights. The MSE learning behavior of the AP(P+1) algorithm with an AR(Q) input (Q ≤ P) is shown to be the same as the NLMS algorithm (μ = 1) with a white input with M-P unity eigenvalues and P zero eigenvalues and increased observation noise. Monte Carlo simulations are presented which support the theoretical results.
منابع مشابه
A Family of Selective Partial Update Affine Projection Adaptive Filtering Algorithms
In this paper we present a general formalism for the establishment of the family of selective partial update affine projection algorithms (SPU-APA). The SPU-APA, the SPU regularized APA (SPU-R-APA), the SPU partial rank algorithm (SPU-PRA), the SPU binormalized data reusing least mean squares (SPU-BNDR-LMS), and the SPU normalized LMS with orthogonal correction factors (SPU-NLMS-OCF) algorithms...
متن کاملDesigning a new multi-objective fuzzy stochastic DEA model in a dynamic environment to estimate efficiency of decision making units (Case Study: An Iranian Petroleum Company)
This paper presents a new multi-objective fuzzy stochastic data envelopment analysis model (MOFS-DEA) under mean chance constraints and common weights to estimate the efficiency of decision making units for future financial periods of them. In the initial MOFS-DEA model, the outputs and inputs are characterized by random triangular fuzzy variables with normal distribution, in which ...
متن کاملانجام یک مرحله پیش پردازش قبل از مرحله استخراج ویژگی در طبقه بندی داده های تصاویر ابر طیفی
Hyperspectral data potentially contain more information than multispectral data because of their higher spectral resolution. However, the stochastic data analysis approaches that have been successfully applied to multispectral data are not as effective for hyperspectral data as well. Various investigations indicate that the key problem that causes poor performance in the stochastic approaches t...
متن کاملImage Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کامل