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20th IFAC Symposium on system identification (SYSID)

作者： Biparva, Darya Materassi, Donatello Univ Minnesota Twin Cities Minneapolis MN 55455 USA

Identifying the underlying structure of a network from observed data is an important problem across various disciplines. Given the general ill-posed nature of the problem, since in many cases, multiple plausible network models can explain the data, this article concentrates on characterizing classes of models providing possible explanations. Specifically, we explore linear models that can account for observed data in the form of wide-sense stationary processes accommodating the potential presence of feedback loops and direct feedthroughs. To achieve this, we leverage key insights from the theory of graphical models. In particular, we extensively employ Pearl-Verma Theorem in causal discovery which allows one to recover all minimal network structures compatible with the observed data. We adapt such a result to deal with stochastic processes and reinterpret it as a Gram-Schmidt orthogonalization procedure in a suitable Hilbert space. This reinterpretation allows us to characterize all minimal networks explaining a set of data, which have the property of not having any algebraic loops. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： system identification and modeling Time series modeling Linear systems

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On affine quasi-LPV system identification with unknown state-scheduling using (deep) Recurrent Neural Networks 26

On affine quasi-LPV System Identification with unknown state...

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26th International Conference on system Theory, Control and Computing (ICSTCC)

作者： Rehmer, Alexander Kroll, Andreas Univ Kassel Kassel Dept Measurement & Control Kassel Germany

ISBN: (数字)9781665467469

ISBN: (纸本)9781665467469

The main contribution of this paper is a model structure for the identification of affine quasi linear parameter-varying (qLPV) models in state-space (SS) form. The special case where the state is unknown and part of the scheduling vector is considered. In this case the model is always recurrent in the states which demands for an internal dynamics approach, e.g. a Recurrent Neural Network (RNN). The proposed approach is based on a structured RNN developed by Lachhab et al. [1], but extends the original model via so-called gates, neural network structures which are responsible for the recent success of RNNs on various areas of application in Machine Learning, such as the Long short-term memory (LSTM) network and the Gated Recurrent Unit (GRU). The use of gates has multiple advantages: The complexity of the models scheduling map and hence its approximation capabilities are increased, while preserving its affine quasi-LPV structure and the applicability of the results on global asymptotic stability (GAS) by Lachhab et al. [1] at the same time. The performance of the proposed approach is demonstrated by comparison with two other RNN-based approaches on two nonlinear system identification benchmark problems.

关键词： system identification and modeling Machine Learning Nonlinear systems LPV

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Evolving temporal convolutional neural networks for modeling turntable servo systems

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Applied Soft Computing 2025年 172卷

作者： Xie, Cheng Xue, Bing Zhang, Mengjie Chen, Songlin Liu, Yang Control and Simulation Center School of Astronautics Harbin Institute of Technology Heilongjiang Harbin150001 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China

Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo performance, some advanced model-based control methods have been used recently, but the control performance is greatly influenced by the accuracy of the model. Therefore, this paper proposes evolving temporal convolutional networks (TCNs) for accurately modeling turntable servo systems. Considering the advantages of TCNs in capturing local dependencies in sequence data and parallel computing, a TCN is adopted to compensate the unmodeled dynamics to significantly improve the accuracy of the existing dynamics model. Then, an evolutionary algorithm (EA) based architecture optimization algorithm for the above TCN model is proposed, and the network performance is continuously improved by simulating the population evolution in nature. Finally, the weight inheritance method is utilized to evaluate the performance of individuals during the evolution process to effectively improve the execution efficiency of the whole algorithm. In addition, the subnets inherited from the supernet meet the real-time requirements of the actual tasks by setting real-time constraints on the supernet. The experimental results demonstrate the superiority of the proposed model against the peer competitors in terms of the prediction performance. In addition, the proposed optimization algorithm also shows higher execution efficiency as verified by ablation experiments. © 2025 Elsevier B.V.

关键词： Evolutionary algorithms (EAs) system identification and modeling Temporal convolutional networks (TCNs) Turntable servo systems

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Zonotopic Reachability Analysis of Multirotor Aircraft 25

Zonotopic Reachability Analysis of Multirotor Aircraft

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25th International Conference on system Theory, Control and Computing (ICSTCC)

作者： Delansnay, Gilles Vande Wouwer, Alain Harno, Hendra G. Kim, Yoonsoo Univ Mons Syst Estimat Control & Optimizat Mons Belgium Gyeongsang Natl Univ Aerosp & Software Engn Jinju Gyeongsangnam D South Korea

ISBN: (纸本)9781665414968

In this work, the reachability analysis of a multirotor aircraft is developed using a zonotopic reachability algorithm, and the results are compared to a Monte Carlo simulation approach. To illustrate this study, a Parrot Mambo minidrone is considered, for which a complete nonlinear Simulink model is available. To achieve the reachability analysis, the closed-loop system can be approximated by a linear invariant system that can easily be exploited. In this way, forward and backward reachable sets can be evaluated and intersected. We determine that only the short-term analysis is necessary as the system is closed-loop and stabilized. For the Monte Carlo simulation analysis, on the other hand, both the original nonlinear model or its linear approximation can be used and we show equivalent results. This study is a preliminary step towards the analysis of upset flight conditions of multirotor aircrafts.

关键词： Linear systems system identification and modeling Embedded systems Zonotope Reachability

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Discrete transfer function modeling of non-linear systems using neural networks 5

Discrete transfer function modeling of non-linear systems us...

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5th International Conference on Image Information Processing (ICIIP)

作者： Sharma, Sudeep Padhy, Prabin Kumar IIITDM Jabalpur Dept Elect & Commun PDPM Jabalpur 482005 Madhya Pradesh India

ISBN: (纸本)9781728108995

ANNs (Artificial Neural Networks) have been used successfully in various system identification applications. However, in general, the ANN-based identification is primarily non-parametric in nature, as the system information is hidden within the neural network architecture, which is not transparent enough to be modeled explicitly. In this paper, a series parallel neural network architecture has been proposed for parametric system identification, whose weights are optimized using adaptive learning through training data, collected from unknown nonlinear system. The optimized weights of the proposed ANN structure are then directly utilized to model the unknown system, in terms of an equivalent discrete transfer function model. The estimated model using proposed technique can be effectively utilized to analyze, test and control the unknown system dynamics. Simulation examples with measurement noise are used to test the effectiveness of proposed identification technique.

关键词： system identification and modeling Neural network Improved Levenberg-Marquardt algorithm

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