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检索条件"任意字段=Conference on Complexity and Nonlinear Dynamics"

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An assessment of modeling dynamics in EEG signals using learned sparse nonlinear representations 30

An assessment of modeling dynamics in EEG signals using lear...

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30th IEEE International conference on Engineering, Technology, and Innovation

作者： Popescu, Dragos Constantin Mateescu, Andrei Stefan, Ioana Livia Vlasceanu, Ioana Miruna Sacala, Joan Stefan Dumitrache, Joan Univ POLITEHN Fac Automat Control & Comp Bucharest Romania

ISBN: (纸本)9798350362442;9798350362435

This work focuses on the challenge of modeling the nonlinear dynamics components of EEG signals, with the long term goal to extract valuable information regarding brain state behavior or emotional and psychological condition. At the core of the approach is the SINDy (Sparse Identification of nonlinear dynamics) method, in which the task of choosing the adequate nonlinear signal transformations and hyperparameters of the sparse regression is accomplished by using a Genetic Algorithm and a Bayesian Optimization. The performance of the two solutions is analyzed and compared against the MindBigData-EP dataset. The first results show that very good accuracy in modeling the dynamics of EEG signals, of over 80% on 60 seconds long recordings, is achieved, however, the precision is a trade-off against the complexity.

关键词： EEG signals sparse nonlinear dynamics SINDy hyperparameters optimization Genetic Algorithm Bayesian Optimization

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Structural Risk Minimization for Learning nonlinear dynamics

Structural Risk Minimization for Learning Nonlinear Dynamics

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American Control conference (ACC)

作者： Stamouli, Charis Chatzipantazis, Evangelos Pappas, George J. Univ Penn Grasp Lab Philadelphia PA 19104 USA

ISBN: (纸本)9798350382662;9798350382655

Recent advances in learning or identification of nonlinear dynamics focus on learning a suitable model within a pre-specified model class. However, a key difficulty that remains is the choice of the model class from which the dynamics will be learned. The fundamental challenge is trading the richness of the model class with the learnability within the model class. Toward addressing the so-called model selection problem, we introduce a novel notion of Structural Risk Minimization (SRM) for learning nonlinear dynamics. Inspired by classical SRM for classification, we minimize a bound on the true prediction error over hierarchies of model classes. The class selected by our SRM scheme is shown to achieve a nearly optimal learning guarantee among all model classes contained in the hierarchy. Employing the proposed scheme along with computable model class complexity bounds, we derive explicit SRM schemes for learning nonlinear dynamics under hierarchies of: i) norm-constrained Reproducing Kernel Hilbert Spaces, and ii) norm-constrained Neural Network classes. We empirically show that even though too loose to be used as absolute estimates of the true prediction error, our SRM bounds are able to track its relative behavior across different model classes of the hierarchy.

关键词： Adversarial machine learning

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Data-driven nonlinear restoring force identification using Gaussian Process regression model for feedback linearization 31

Data-driven nonlinear restoring force identification using G...

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31st International conference on Noise and Vibration Engineering, ISMA 2024 and 10th International conference on Uncertainty in Structural dynamics, USD 2024

作者： Paixão, J. Platzer, A. Rodriguez, J. Blal, N. Baguet, S. Université de Lyon CNRS INSA-Lyon LaMCoS UMR5259 Villeurbanne69621 France

ISBN: (纸本)9789082893175

The design of active controllers for vibration attenuation in mechanical systems depends on an accurate identification of the system. This task becomes very challenging in nonlinear systems due to the complexity and variety of nonlinear phenomena, and where a parametric modeling approach may have difficulty in capturing the full dynamic behavior. In this paper, we investigate the use of a data-driven identification approach using a Gaussian process regression model for feedback linearization control of nonlinear dynamics systems. The proposed methodology is investigated in a numerical demonstration case of a Duffing oscillator. The advantages and limitations of the data-driven identification approach are discussed with respect to the feedback linearization control aspect. © 2024 Proceedings of ISMA 2024 - International conference on Noise and Vibration Engineering and USD 2024 - International conference on Uncertainty in Structural dynamics. All rights reserved.

关键词： Vibrations (mechanical)

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A methodology to manage the complexity of a nonlinear multibody digital twin in railway applications 31

A methodology to manage the complexity of a nonlinear multib...

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31st International conference on Noise and Vibration Engineering, ISMA 2024 and 10th International conference on Uncertainty in Structural dynamics, USD 2024

作者： Auzanneau, B. Sadoulet-Reboul, E. Foltête, E. Ham-Livet, G. Cogan, S. Université de Franche-Comté FEMTO-ST Département de Mécanique Appliquée France ALSTOM Transport SA Le Creusot France

ISBN: (纸本)9789082893175

In the railway industry, digital twins based on nonlinear multibody simulations are developed to provide decision-making support for bogie design. A recurring question is to find an acceptable compromise between model complexity and computational burden to ensure an accurate representation of the global train dynamic behavior. The objective of the research work is to investigate different modeling strategies integrating the complexity of a physical model, and to discriminate between the effectiveness of these strategies in faithfully reproducing the dynamic responses of a structure. Two approaches are investigated to study a real yaw damper component of a motor bogie. Firstly, a rheological model representing the dynamic behavior of the damper is proposed and is integrated into dynamic simulations for sensitivity studies in order to identify the influential elements. In a second phase, an alternative model based on a multi-layer perceptron neural network is proposed to improve the computational efficiency of the digital twin. © 2024 Proceedings of ISMA 2024 - International conference on Noise and Vibration Engineering and USD 2024 - International conference on Uncertainty in Structural dynamics. All rights reserved.

关键词： Bogies (railroad rolling stock)

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A noise-robust approach to estimate dimension of sleep EEG in mice using permutation entropy

A noise-robust approach to estimate dimension of sleep EEG i...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE)

作者： Koyama, Kazuki Sakaguchi, Masanori Ohnishi, Takaaki Rikkyo Univ Grad Sch Artificial Intelligence & Sci Tokyo Japan Univ Tsukuba Int Inst Integrat Sleep Med WPI IIIS Ibaraki Japan

ISBN: (纸本)9798350319552;9798350319545

Generally, an electroencephalogram (EEG) follows nonlinear dynamics because it shows complex regularities. The analysis of nonlinear dynamics using the 'embedding' method requires the determination of parameters such as embedding dimension and time delay. However, we cannot obtain correct parameters from a conventional method, such as correlation integration, due to noise. Therefore, we use permutation entropy as a robust method against noise. This paper estimates the EEG parameters of mice during different sleep stages by using permutation entropy. The study results suggest that the embedding dimension and time delay are 5 and 8 ms, respectively, regardless of the sleep stage. Furthermore, we analyze the EEG of a sleeping mouse from the nonlinear dynamics. Permutation entropy can distinguish between rapid eye movement sleep and wakefulness, unlike linear methods such as power spectrum density.

关键词： nonlinear dynamics chaos permutation entropy complexity EEG

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Rademacher complexity of Neural ODEs via Chen-Fliess Series 6

Rademacher Complexity of Neural ODEs via Chen-Fliess Series

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6th Annual Learning for dynamics and Control conference

作者： Hanson, Joshua Raginsky, Maxim Univ Illinois 1308 W Main St Urbana IL 61801 USA

We show how continuous-depth neural ODE models can be framed as single-layer, infinite-width nets using the Chen-Fliess series expansion for nonlinear ODEs. In this net, the output "weights" are taken from the signature of the control input - a tool used to represent infinite-dimensional paths as a sequence of tensors - which comprises iterated integrals of the control input over a simplex. The "features" are taken to be iterated Lie derivatives of the output function with respect to the vector fields in the controlled ODE model. The main result of this work applies this framework to derive compact expressions for the Rademacher complexity of ODE models that map an initial condition to a scalar output at some terminal time. The result leverages the straightforward analysis afforded by single-layer architectures. We conclude with some examples instantiating the bound for some specific systems and discuss potential follow-up work.

关键词： Neural ODE Chen-Fliess series Rademacher complexity generalization bound

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A Control Approach for nonlinear Stochastic State Uncertain Systems with Probabilistic Safety Guarantees

A Control Approach for Nonlinear Stochastic State Uncertain ...

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American Control conference (ACC)

作者： Ramadan, Mohammad S. Alsuwaidan, Mohammad Atallah, Ahmed Herbert, Sylvia Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA USA

ISBN: (纸本)9798350382662;9798350382655

This paper presents an algorithm to apply nonlinear control design approaches to the case of stochastic systems with partial state observation. Deterministic nonlinear control approaches are formulated under the assumption of full state access and, often, relative degree one. We propose a control design approach that initially generates a control policy for nonlinear deterministic models with full state observation. The resulting control policy is then employed to construct an importance-like probability distribution over the space of control sequences which are to be evaluated for the true stochastic and state-uncertain dynamics. In the sampling step of a random search control optimization procedure, this distribution serves to focus the exploration effort on certain regions of the control space. The sampled control sequences are assigned costs determined by a prescribed finite-horizon performance and safety measure that considers stochastic dynamics. This sampling algorithm is parallelizable, exhibits computational complexity indifferent to the state dimension, and probabilistically guarantees safety over the prescribed prediction horizon. A numerical simulation is provided to compare the presented approach to the certainty equivalence controller.

关键词： Stochastic systems

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nonlinear behaviours of a class of inertial amplifiers 31

Nonlinear behaviours of a class of inertial amplifiers

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31st International conference on Noise and Vibration Engineering, ISMA 2024 and 10th International conference on Uncertainty in Structural dynamics, USD 2024

作者： Shaw, A. Nar, E. Adhikari, S. Swansea University Department of Aerospace Engineering Bay Campus Fabian Way SwanseaSA1 8EN United Kingdom University of Bristol School of Electrical Electronic and Mechanical Engineering Queens Building University Walk BristolBS8 1TR United Kingdom University of Glasgow James Watt School of Engineering University Avenue GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9789082893175

Inertial amplifiers (IAs) mechanically enhance the inertia of a dynamic system, without requiring similar increases in system mass, with potential applications ranging from vibration isolation to energy harvesting and sensing, in particular for atomic force microscopy. This work begins by briefly presenting experimental results that confirm the ability of the IA to vary the apparent mass of a dynamic system to a remarkable degree with simple alterations to its configuration. It then shows that high inertial amplification factors inevitably lead to significant nonlinearities within the IA. However despite the complexity of the nonlinear terms involved, in many cases they can be simply expressed in the form α (x) ẍ + β(x)(Equation presented)2, to gain insight into system behaviour. The work goes on to highlight the nonlinear behaviour of the system through quiver diagrams, and traces its nonlinear normal modes (NNMs) through a backbone analysis. Finally the effects of forcing and damping are uncovered using a mutliharmonic harmonic balance technique. © 2024 Proceedings of ISMA 2024 - International conference on Noise and Vibration Engineering and USD 2024 - International conference on Uncertainty in Structural dynamics. All rights reserved.

关键词： Structural dynamics

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Convolutional neural networks applied to nonlinear dynamical systems: a Wiener series perspective 31

Convolutional neural networks applied to nonlinear dynamical...

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31st International conference on Noise and Vibration Engineering, ISMA 2024 and 10th International conference on Uncertainty in Structural dynamics, USD 2024

作者： Massingham, J. Nielsen, O. Butlin, T. University of Cambridge Department of Engineering United Kingdom Bose Corporation FraminghamMA United States

ISBN: (纸本)9789082893175

Accurately predicting the response of nonlinear dynamical systems subject to random excitation is a challenging task. Machine learning models, such as convolutional neural networks (CNNs), have demonstrated state-of-the-art performance in many branches of engineering. However, for many nonlinear systems, they are unable to capture the entire response and their performance is difficult to interpret. Identifying the improvement in performance that could be achieved through the collection of additional data or increasing the size of the model would be very useful. In this paper, the Gaussian process generalised Wiener series (GPGWS) is used to analyse the performance of CNNs applied to both a numerical and an experimental case study. The GPGWS is a recently developed method that enables the calculation of the Wiener series decomposition up to and including the third order Wiener kernel. The Wiener series sheds light on the underlying complexity of the system response and provides insight into the learning process of the CNN. © 2024 Proceedings of ISMA 2024 - International conference on Noise and Vibration Engineering and USD 2024 - International conference on Uncertainty in Structural dynamics. All rights reserved.

关键词： nonlinear dynamical systems

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Adaptive Command Filtered Backstepping Control of Connected Vehicles with nonlinear Disturbances 35

Adaptive Command Filtered Backstepping Control of Connected ...

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35th Chinese Control and Decision conference (CCDC)

作者： Li, Yixuan Wen, Shixi Zhao, Yuan Dalian Univ Sch Informat & Engn Dalian Peoples R China

ISBN: (纸本)9798350334722

This paper investigates the problem of adaptive control of connected vehicles subject to uncertain nonlinearity. A third-order nonlinear error model is established, which considers the effect of nonlinear vehicle dynamics and unknown acceleration disturbances. To deal with the nonlinear dynamics and uncertainty, the command filtering technique is used to design the controller, where the radial basis function neural networks (RBFNNs) is introduced to estimate and compensate for the uncertainty. It is evident that the problem of "explosion of complexity" is solved when the backstepping method is involved. Both the individual vehicle stability and the disturbance string stability can be guaranteed by the designed controller. The effectiveness of the proposed method is illustrated by the numerical simulations.

关键词： Vehicle platoons command filter nonlinear disturbances RBFNNs disturbance string stability

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