Improving Kalman filtering by input selection for nonuniformly observable state-affine systems

2016 European Control Conference (ECC) ◽

10.1109/ecc.2016.7810445 ◽

2016 ◽

Author(s):

Ignacio Rubio Scola ◽

Gildas Besancon ◽

Didier Georges

Keyword(s):

Kalman Filtering ◽

Affine Systems ◽

Input Selection ◽

Observable State ◽

Selection For

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Finite-horizon input selection for system identification

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475) ◽

10.1109/cdc.2003.1272390 ◽

2004 ◽

Cited By ~ 2

Author(s):

S.L. Lacy ◽

D.S. Bernstein ◽

R.S. Erwin

Keyword(s):

System Identification ◽

Finite Horizon ◽

Input Selection ◽

Selection For

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Neural model input selection for a MIMO chemical process

Engineering Applications of Artificial Intelligence ◽

10.1016/s0952-1976(99)00046-9 ◽

2000 ◽

Vol 13 (1) ◽

pp. 15-23 ◽

Cited By ~ 26

Author(s):

D.L. Yu ◽

J.B. Gomm ◽

D. Williams

Keyword(s):

Chemical Process ◽

Neural Model ◽

Input Selection ◽

Model Input ◽

Selection For

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Submodular Input Selection for Synchronization in Kuramoto Networks

2020 59th IEEE Conference on Decision and Control (CDC) ◽

10.1109/cdc42340.2020.9304102 ◽

2020 ◽

Author(s):

Dinuka Sahabandu ◽

Andrew Clark ◽

Linda Bushnell ◽

Radha Poovendran

Keyword(s):

Input Selection ◽

Selection For

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Optimal input selection for neural fuzzy modelling with application to Charpy energy prediction

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) ◽

10.1109/fuzzy.2011.6007735 ◽

2011 ◽

Cited By ~ 2

Author(s):

Yong Y. Yang ◽

Mahdi Mahfouf ◽

Qian Zhang

Keyword(s):

Fuzzy Modelling ◽

Input Selection ◽

Energy Prediction ◽

Optimal Input ◽

Selection For ◽

Neural Fuzzy

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Input selection for disturbance rejection under manipulated variable constraints

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)87535-7 ◽

1997 ◽

Vol 21 ◽

pp. S403-S408 ◽

Cited By ~ 18

Author(s):

Yi Cao ◽

Diane Rossiter ◽

David Owens

Keyword(s):

Disturbance Rejection ◽

Input Selection ◽

Selection For

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Non-Linear Spatio-Temporal Input Selection for Rainfall Forecasting Using Recurrent Neural Networks

2018 International Seminar on Intelligent Technology and Its Applications (ISITIA) ◽

10.1109/isitia.2018.8710864 ◽

2018 ◽

Author(s):

Ahmad Saikhu ◽

Agus Zainal Arifin ◽

Chastine Fatichah

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Rainfall Forecasting ◽

Input Selection ◽

Non Linear ◽

Selection For ◽

Spatio Temporal

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Input Selection for Long-Term Prediction of Time Series

Computational Intelligence and Bioinspired Systems - Lecture Notes in Computer Science ◽

10.1007/11494669_123 ◽

2005 ◽

pp. 1002-1009 ◽

Cited By ~ 4

Author(s):

Jarkko Tikka ◽

Jaakko Hollmén ◽

Amaury Lendasse

Keyword(s):

Time Series ◽

Input Selection ◽

Term Prediction ◽

Selection For ◽

Long Term Prediction

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Driver input selection for main-memory multi-way joins

Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC '13 ◽

10.1145/2480362.2480521 ◽

2013 ◽

Cited By ~ 1

Author(s):

Emmanouil Valsomatzis ◽

Anastasios Gounaris

Keyword(s):

Main Memory ◽

Input Selection ◽

Selection For

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A New Computational Method of Input Selection for Stock Market Forecasting with Neural Networks

Computational Science – ICCS 2006 - Lecture Notes in Computer Science ◽

10.1007/11758549_46 ◽

2006 ◽

pp. 308-315 ◽

Cited By ~ 2

Author(s):

Wei Huang ◽

Shouyang Wang ◽

Lean Yu ◽

Yukun Bao ◽

Lin Wang

Keyword(s):

Neural Networks ◽

Stock Market ◽

Computational Method ◽

Input Selection ◽

Stock Market Forecasting ◽

Selection For

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Input Selection for Nonlinear Regression Models

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2004.834810 ◽

2004 ◽

Vol 12 (5) ◽

pp. 688-696 ◽

Cited By ~ 65

Author(s):

R. Sindelar ◽

R. Babuska

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Input Selection ◽

Nonlinear Regression Models ◽

Selection For

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