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ORCIDPawel Wachel
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2020 – today
- 2024
[j13]Pawel Wachel
, Krzysztof Kowalczyk, Cristian R. Rojas:
Decentralized diffusion-based learning under non-parametric limited prior knowledge. Eur. J. Control 75: 100912 (2024)- [j12]Gabriel Maik, Grzegorz Mzyk, Pawel Wachel:
Exponential excitations for effective identification of Wiener system. Int. J. Control 97(9): 1956-1966 (2024) - [c13]Krzysztof Kowalczyk, Pawel Wachel, Cristian R. Rojas:
Kernel-Based Learning with Guarantees for Multi-agent Applications. ICCS (3) 2024: 479-487 - [i6]Krzysztof Kowalczyk, Pawel Wachel, Cristian R. Rojas:
Kernel-based learning with guarantees for multi-agent applications. CoRR abs/2404.09708 (2024) - 2023
- [j11]Pawel Wachel, Koen Tiels, Maciej Filinski:
Learning low-dimensional separable decompositions of MIMO non-linear systems. Int. J. Control 96(4): 900-906 (2023) - [j10]Szymon Lagosz, Pawel Wachel, Przemyslaw Sliwinski:
A Dual Averaging Algorithm for Online Modeling of Infinite Memory Nonlinear Systems. IEEE Trans. Autom. Control. 68(9): 5677-5684 (2023) - [c12]Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Pawel Wachel:
A Computationally Lightweight Safe Learning Algorithm. CDC 2023: 1022-1027 - [i5]Pawel Wachel, Krzysztof Kowalczyk, Cristian R. Rojas:
Decentralized diffusion-based learning under non-parametric limited prior knowledge. CoRR abs/2305.03295 (2023) - [i4]Krzysztof Zajac, Pawel Wachel:
Frequency-Supported Neural Networks for Nonlinear Dynamical System Identification. CoRR abs/2305.06344 (2023) - [i3]Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Pawel Wachel:
A computationally lightweight safe learning algorithm. CoRR abs/2309.03672 (2023) - 2022
- [j9]Cristian R. Rojas, Pawel Wachel:
On State-Space Representations of General Discrete-Time Dynamical Systems. IEEE Trans. Autom. Control. 67(12): 6975-6979 (2022) - [i2]Cristian R. Rojas, Pawel Wachel:
On state-space representations of general discrete-time dynamical systems. CoRR abs/2205.03366 (2022) - 2021
- [j8]Szymon Lagosz, Przemyslaw Sliwinski, Pawel Wachel:
Identification of Wiener-Hammerstein systems by ℓ1-constrained Volterra series. Eur. J. Control 58: 53-59 (2021) - [c11]Przemyslaw Sliwinski, Pawel Wachel, Jerzy W. Rozenblit:
Mimicking Learning for 1-NN Classifiers. ICCS (3) 2021: 75-80 - [c10]Maciej Filinski, Pawel Wachel, Koen Tiels:
Low-Dimensional Decompositions for Nonlinear Finite Impulse Response Modeling. ICCS (5) 2021: 352-359
2010 – 2019
- 2019
- [j7]Agnieszka Kazimierska, Michal M. Placek, Agnieszka Uryga, Pawel Wachel, Malgorzata Burzynska, Magdalena Kasprowicz:
Assessment of Baroreflex Sensitivity Using Time-Frequency Analysis during Postural Change and Hypercapnia. Comput. Math. Methods Medicine 2019: 4875231:1-4875231:17 (2019) - [c9]Pawel Wachel, Przemyslaw Sliwinski, Zygmunt Hasiewicz:
Modeling of switching-mode nonlinear system by exponentially weighted aggregation. MMAR 2019: 554-557 - 2018
- [i1]Przemyslaw Sliwinski, Anna Marconato, Pawel Wachel, Georgios Birpoutsoukis:
Nonlinear system modeling based on constrained Volterra series estimates. CoRR abs/1804.07258 (2018) - 2017
- [j6]Grzegorz Mzyk, Pawel Wachel:
Kernel-based identification of Wiener-Hammerstein system. Autom. 83: 275-281 (2017) - [j5]Pawel Wachel:
Wiener system modelling by exponentially weighted aggregation. Int. J. Control 90(11): 2480-2489 (2017) - [c8]Zygmunt Hasiewicz, Pawel Wachel, Grzegorz Mzyk, Bartlomiej Kozdras:
Multistage identification of Wiener-Hammerstein system. KKA 2017: 527-535 - [c7]Pawel Wachel, Zygmunt Hasiewicz:
Exponentially weighted aggregation of models for Wiener-Hammerstein system modelling. MMAR 2017: 826-830 - 2016
- [j4]Pawel Wachel:
Convex aggregative modelling of infinite memory nonlinear systems. Int. J. Control 89(8): 1613-1621 (2016) - [c6]Szymon Lagosz, Przemyslaw Sliwinski, Pawel Wachel:
Aggregative modeling of Wiener systems. MMAR 2016: 375-378 - [c5]Adrian Galeziowski, Przemyslaw Sliwinski, Pawel Wachel:
A stochastic approach to contrast detection autofocusing. A 2D case analysis. MMAR 2016: 738-743 - 2015
- [j3]Pawel Wachel, Przemyslaw Sliwinski:
Aggregative Modeling of Nonlinear Systems. IEEE Signal Process. Lett. 22(9): 1482-1486 (2015) - [c4]Michal M. Placek, Pawel Wachel, Marek Czosnyka, Martin Soehle, Peter Smielewski, Magdalena Kasprowicz:
Complexity of cerebral blood flow velocity and arterial blood pressure in subarachnoid hemorrhage using time-frequency analysis. EMBC 2015: 7700-7703 - 2013
- [j2]Przemyslaw Sliwinski, Zygmunt Hasiewicz, Pawel Wachel:
A simple scheme for semi-recursive identification of Hammerstein system nonlinearity by Haar wavelets. Int. J. Appl. Math. Comput. Sci. 23(3): 507-520 (2013) - [c3]Zygmunt Hasiewicz, Grzegorz Mzyk, Przemyslaw Sliwinski, Pawel Wachel:
Model Selection of Hammerstein System Nonlinearity under Heavy Noise. ALCOSP 2013: 378-383 - [c2]Przemyslaw Sliwinski, Krzysztof S. Berezowski, Pawel Wachel, Gilles Sicard, Laurent Fesquet:
Empirical Recovery of Input Nonlinearity in Distributed Element Models. ALCOSP 2013: 617-622 - [c1]Przemyslaw Sliwinski, Pawel Wachel:
Application of stochastic counterpart optimization to contrast-detection autofocusing. ICACCI 2013: 333-337
2000 – 2009
- 2007
- [j1]Miroslaw Pawlak, Zygmunt Hasiewicz, Pawel Wachel:
On Nonparametric Identification of Wiener Systems. IEEE Trans. Signal Process. 55(2): 482-492 (2007)
Coauthor Index
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