Identification of nonlinear systems based on extreme learning machine

Grzeidak, Emerson

doi:10.26512/2016.06.d.21603

DOI: 10.26512/2016.06.d.21603

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Identification of nonlinear systems based on extreme learning machine

Emerson Grzeidak¹

Abstract: O resultado das simulações para o sistema caótico unificado mostram a eficácia e o desempenho da abordagem proposta na presença de distúrbios. Adicionalmente, para mostrar a aplicabilidade do algoritmo de aprendizado proposto para sistemas com várias dimensões, uma simulação com um sistema hipercaótico complexo é exibida. Finalmente, uma comparação do algoritmo proposto com [2] é realizada para mostrar as vantagens e peculiaridades do método proposto na presença de distúrbios. Capítulo 6 resume as contribuiçõe… Show more

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“…The model structure and parameters are unknown, and the model architecture is not based on physical laws. As a result, the parameters often lack physical meaning or interpretation [1,26].…”

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confidence: 99%

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

Ayankoso,

Olejnik

2023

Electronics

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Developing accurate dynamic models for various systems is crucial for optimization, control, fault diagnosis, and prognosis. Recent advancements in information technologies and computing platforms enable the acquisition of input–output data from dynamical systems, resulting in a shift from physics-based methods to data-driven techniques in science and engineering. This review examines different data-driven modeling approaches applied to the identification of mechanical and electronic systems. The approaches encompass various neural networks (NNs), like the feedforward neural network (FNN), convolutional neural network (CNN), long short-term memory (LSTM), transformer, and emerging machine learning (ML) techniques, such as the physics-informed neural network (PINN) and sparse identification of nonlinear dynamics (SINDy). The main focus is placed on applying these techniques to real-world problems. A real application is presented to demonstrate the effectiveness of different machine learning techniques, namely, FNN, CNN, LSTM, transformer, SINDy, and PINN, in data-driven modeling and the identification of a geared DC motor. The results show that the considered ML techniques (traditional and state-of-the-art methods) perform well in predicting the behavior of such a classic dynamical system. Furthermore, SINDy and PINN models stand out for their interpretability compared to the other data-driven models examined. Our findings explicitly show the satisfactory predictive performance of six different ML models while also highlighting their pros and cons, such as interpretability and computational complexity, using a real-world case study. The developed models have various applications and potential research areas are discussed.

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“…The model structure and parameters are unknown, and the model architecture is not based on physical laws. As a result, the parameters often lack physical meaning or interpretation [1,26].…”

mentioning

confidence: 99%

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

Ayankoso,

Olejnik

2023

Electronics

View full text Add to dashboard Cite

show abstract

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Identification of nonlinear systems based on extreme learning machine

Cited by 1 publication

References 96 publications

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

Time-Series Machine Learning Techniques for Modeling and Identification of Mechatronic Systems with Friction: A Review and Real Application

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