A dynamic neural network model for nonlinear system identification

Wang, Chi-Hsu; Chen, Pin-Cheng; Lin, Ping-Zong; Lee, Tsu‐Tian

doi:10.1109/iri.2009.5211647

Cited by 3 publications

(1 citation statement)

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“…Such models don't reflect the dynamic and nonlinear properties of a real object, so they cannot provide high identification accuracy [2]. An up-to-date approach to modeling nonlinear dynamic objects is the artificial neural network apparatus [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Use of Dynamic Neural Networks for Modeling Nonlinear Objects with Significant Nonlinearity

Fomin,

Polozhaenko,

Krykun

et al. 2023

Annals of Computer Science and Information Systems

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The work is devoted to the problem of nonlinear modeling of objects based on dynamic neural networks. The aim of the work is to improve the accuracy of modeling dynamic objects with significant nonlinearities using neural network models, and identify the scope of their effective application. This aim is achieved by applying the dynamic nonlinear models in the form of time delay neural networks. The scientific novelty of the work lies in the determination of the dependences between the accuracy of suggested models and the types of model input signals, as well as the amplitudes of model input signals. Practical usefulness of the research lies in the determination of the area of effective use of suggested models of dynamic objects with significantly nonlinear features, such us saturation. Significance of the obtained results: the application of the proposed models for identification dynamic objects with significantly nonlinear characteristics allows to improve the accuracy of the modelling process in comparison with models based on deterministic identification methods, such as integro-power series based on multidimensional weight functions.

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Section: Introductionmentioning

confidence: 99%

Use of Dynamic Neural Networks for Modeling Nonlinear Objects with Significant Nonlinearity

Fomin,

Polozhaenko,

Krykun

et al. 2023

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

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Machine Learning Based Simulation for Design Space Exploration

2022

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Design of software in the automotive domain often involves simulation to allow early software parametrization. Modeling complex systems or components impacted by the software in an analytical way can be time-consuming, require domain knowledge and executing the analytical models can result in high computational effort. In specific applications, these challenges can be overcome by applying machine learning based simulation. This contribution presents results of a case study in which powertrain components are modeled data-driven with artificial neural networks to support design space exploration

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Dynamic Neural Network-based System Identification of a Pressurized Water Reactor

Naimi

Deng

Altahhan

et al. 2020

2020 8th International Conference on Control, Mechatronics and Automation (ICCMA)

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This work presents a dynamic neural networkbased (DNN) system identification approach for a pressurized water nuclear reactor. The presented empirical modelling approach describes the DNN structure using differential equations. Local optimization algorithms based on unconstrained Quasi-Newton and interior point approaches are used in the identification process. The efficacy of the proposed approach has been demonstrated by identifying a nuclear reactor core coupled with thermal-hydraulics. DNNs are employed to train the structure and validate it using the nuclear reactor data. The simulation results show that the neural network identified model is sufficiently able to capture the dynamics of the nuclear reactor and it is suitably able to approximate the complex nuclear reactor system.

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A dynamic neural network model for nonlinear system identification

Cited by 3 publications

References 0 publications

Use of Dynamic Neural Networks for Modeling Nonlinear Objects with Significant Nonlinearity

Use of Dynamic Neural Networks for Modeling Nonlinear Objects with Significant Nonlinearity

Machine Learning Based Simulation for Design Space Exploration

Dynamic Neural Network-based System Identification of a Pressurized Water Reactor

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