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Název:
Multivariate Anomaly Detection System with System-Dynamics-Reflective Neural Architectures
Autoři: HRUBÝ, Filip
Typ dokumentu: Diplomové práce
Rok: 2024
Jazyk: eng
Abstrakt: The aim of this thesis is to propose and explore machine learning based anomaly detection in dynamical systems for industrial applications where training data is limited and subject to noise and disturbances. The focus is on neural network architectures that reflect the system dynamics, i.e. where analogies can be drawn to the state space representation of dynamic systems in a discrete-time domain. A comparison of such architectures with conventional neural networks such as multilayer perceptron or LSTM networks is shown. First, the background methods are defined, followed by an examination of the different neural architectures and learning algorithms and their comparison. Then, the dynamical system for generating data is described and data sets with anomalies are generated. The core of the contribution is the proposed method for anomaly detection via a heat map of the neural weight behaviour over time and its investigation and comparison for different networks and learning algorithms. Finally, the results of the algorithms are compared based on the analysis and discussion, and the feasibility of the approach is evaluated.
Klíčová slova: anomaly detection; dynamical systems; explainable AI; gradient descent; heatmap; higher-order neural units; in-parameter-linear nonlinear neural architectures; Levenberg-Marquart; physics informed machine learning; shallow neural networks
Citace: HRUBÝ, Filip. Multivariate Anomaly Detection System with System-Dynamics-Reflective Neural Architectures . České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Instituce: Jihočeská univerzita v Českých Budějovicích (web)
Informace o dostupnosti dokumentu: Plný text je dostupný v digitálním repozitáři JČU.
Původní záznam: http://www.jcu.cz/vskp/75389
Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-653109
Autoři: HRUBÝ, Filip
Typ dokumentu: Diplomové práce
Rok: 2024
Jazyk: eng
Abstrakt: The aim of this thesis is to propose and explore machine learning based anomaly detection in dynamical systems for industrial applications where training data is limited and subject to noise and disturbances. The focus is on neural network architectures that reflect the system dynamics, i.e. where analogies can be drawn to the state space representation of dynamic systems in a discrete-time domain. A comparison of such architectures with conventional neural networks such as multilayer perceptron or LSTM networks is shown. First, the background methods are defined, followed by an examination of the different neural architectures and learning algorithms and their comparison. Then, the dynamical system for generating data is described and data sets with anomalies are generated. The core of the contribution is the proposed method for anomaly detection via a heat map of the neural weight behaviour over time and its investigation and comparison for different networks and learning algorithms. Finally, the results of the algorithms are compared based on the analysis and discussion, and the feasibility of the approach is evaluated.
Klíčová slova: anomaly detection; dynamical systems; explainable AI; gradient descent; heatmap; higher-order neural units; in-parameter-linear nonlinear neural architectures; Levenberg-Marquart; physics informed machine learning; shallow neural networks
Citace: HRUBÝ, Filip. Multivariate Anomaly Detection System with System-Dynamics-Reflective Neural Architectures . České Budějovice, 2024. diplomová práce (Mgr.). JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH. Přírodovědecká fakulta
Instituce: Jihočeská univerzita v Českých Budějovicích (web)
Informace o dostupnosti dokumentu: Plný text je dostupný v digitálním repozitáři JČU.
Původní záznam: http://www.jcu.cz/vskp/75389
Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-653109
Záznam vytvořen dne 2024-09-28, naposledy upraven 2024-09-28.