Combined Kalman and sliding innovation filtering: An adaptive estimation strategy

Author:

Lee Andrew S.,Hilal Waleed,Andrew Gadsden S.,Al-Shabi M.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference37 articles.

1. Gaussian filters for parameter and state estimation: a general review of theory and recent trends;Afshari;Signal Process.,2017

2. Kalman Filtering and Neural Networks,2001

3. Neural network training loss optimization utilizing the sliding innovation filter;Alsadi,2022

4. Novel model-based estimators for the purposes of fault detection and diagnosis;Gadsden;IEEE/ASME Trans. Mechatron.,2013

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