On the Stochastic Modeling of the NSVR–IAF–PNLMS Algorithm for Correlated Gaussian Input Data

Journal of control, automation and electrical systems

article

Kuhn, Eduardo Vinicius¹  Perez, Fábio Luis³  de Souza, Francisco das Chagas⁴  Seara, Rui¹ 

[1] LINSE: Circuits and Signal Processing Laboratory, Department of Electrical and Electronics Engineering, Federal University of Santa Catarina;LAPSE: Signal Processing and Electronics Laboratory, Department of Electronics Engineering, Federal University of Technology - Paraná;ASL: Adaptive Filtering and Control Systems Laboratory, Department of Telecommunications and Electrical Engineering, University of Blumenau;LSAPS: Adaptive Systems and Signal Processing Laboratory, Department of Electrical Engineering, Federal University of Maranhão

关键词: Adaptive filtering;    Proportionate normalized least-mean-square algorithm;    Stochastic model;   

DOI  :  10.1007/s40313-019-00553-z

学科分类：自动化工程

来源: Springer

【 摘 要 】

This paper presents a stochastic model for the normalized smoothed variation rate individual-activation-factor proportionate normalized least-mean-square (NSVR–IAF–PNLMS) algorithm. Specifically, taking into account correlated Gaussian input data, model expressions are derived for predicting the mean weight vector, gain distribution matrix, NSVR metric, learning curve, weight-error correlation matrix, and steady-state excess mean-square error. Such expressions are obtained by considering the time-varying characteristics of the gain distribution matrix. Simulation results are shown confirming the accuracy of the proposed model for different operating conditions.

【 授权许可】

CC BY