Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter-Reference-Cited by-同舟云学术

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Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter

Published:2016-01 Issue: Volume:173 Page:645-658
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Pak Jung Min,Ahn Choon Ki,Shi Peng,Lim Myo Taeg

Funder

National Research Foundation of Korea (NRF)

Ministry of Science, ICT & Future Planning

Korea Institute of Energy Technology Evaluation and Planning

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference40 articles.

1. A new approach to linear filtering and prediction problems;Kalman;Trans. ASME J. Basic Eng.,1960

2. Fuzzy Kalman-type filter for interval fractional-order systems with finite-step auto-correlated process noises;Pourdehi;Neurocomputing,2015

3. FPGA implementation of Kalman filter for neural ensemble decoding of rat׳s motor cortex;Zhu;Neurocomputing,2011

4. Efficient online recurrent connectionist learning with the ensemble Kalman filter;Mirikitani;Neurocomputing,2010

5. A square root formulation of the Kalman Schmidt filter;Bellantoni;AIAA J.,1967

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