Adaptive Algorithm for Estimation of Two-Dimensional Autoregressive Fields from Noisy Observations | Zendy

Alimorad Mahmoudi | Zendy

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Adaptive Algorithm for Estimation of Two-Dimensional Autoregressive Fields from Noisy Observations

Author(s) -

Alimorad Mahmoudi

Publication year - 2014

Publication title -

international journal of stochastic analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.19

H-Index - 28

eISSN - 2090-3340

pISSN - 2090-3332

DOI - 10.1155/2014/247274

Subject(s) - autoregressive model , mathematics , algorithm , gradient descent , estimation , method of steepest descent , mathematical optimization , computer science , artificial intelligence , artificial neural network , statistics , management , economics

This paper deals with the problem of two-dimensional autoregressive (AR) estimation from noisy observations. The Yule-Walker equations are solved using adaptive steepest descent (SD) algorithm. Performance comparisons are made with other existing methods to demonstrate merits of the proposed method

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