| 1 |
BIAS OF PARTICLE APPROXIMATIONS TO OPTIMAL FILTER DERIVATIVE
Tadic VZB, Doucet A
SIAM Journal on Control and Optimization, 59(1), 727, 2021 |
| 2 |
UD-Based Pairwise and MIMO Kalman-Like Filtering for Estimation of Econometric Model Structures
Kulikova MV, Tsyganova JV, Kulikov GY
IEEE Transactions on Automatic Control, 65(10), 4472, 2020 |
| 3 |
Design of measurement difference autocovariance method for estimation of process and measurement noise covariances
Dunik J, Kost O, Straka O
Automatica, 90, 16, 2018 |
| 4 |
A simple multi-model prediction method
Strutzel FAM, Bogle IDL
Chemical Engineering Research & Design, 138, 51, 2018 |
| 5 |
Conversion of descriptor representations to state-space form: an extension of the shuffle algorithm
Galvao RKH, Kienitz KH, Hadjiloucas S
International Journal of Control, 91(10), 2199, 2018 |
| 6 |
On site characterisation of the overall heat loss coefficient: Comparison of different assessment methods by a blind validation exercise on a round robin test box
Roels S, Bacher P, Bauwens G, Castatino S, Jimenez MJ, Madsen H
Energy and Buildings, 153, 179, 2017 |
| 7 |
An Auxiliary Particle Filtering Algorithm With Inequality Constraints
Li BB, Liu CJ, Chen WH
IEEE Transactions on Automatic Control, 62(9), 4639, 2017 |
| 8 |
Short-term ensemble forecast for purchased photovoltaic generation
Takeda H
Solar Energy, 149, 176, 2017 |
| 9 |
N2SID: Nuclear norm subspace identification of innovation models
Verhaegen M, Hansson A
Automatica, 72, 57, 2016 |
| 10 |
Using the ensemble Kalman filter for electricity load forecasting and analysis
Takeda H, Tamura Y, Sato S
Energy, 104, 184, 2016 |
