Enhancing BNN structure learning of hybrid modeling strategy for free formulated mechanism complex systems

Liang, Mingyu; Li, Shaoyuan

doi:10.1016/j.jprocont.2022.10.006

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Hybrid architectures aim to model complex, nonlinear behaviors, systems with non-constant parameters or non-stationary processes. Hybrid models for complex nonlinear systems have been proposed in areas such as manufacturing [15]; the food industry [16]; the chemical/oil/gas industry [17][18][19]; and biological systems [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Aguilar,

Chanal,

Chamagne

et al. 2024

Energies

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The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Aguilar,

Chanal,

Chamagne

et al. 2024

Energies

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Multi-view graph convolutional network with comprehensive structural learning: Enhancing dynamics representation for industrial processes

Mou,

Liu,

Zou

et al. 2024

Journal of Process Control

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Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

Ul Arifeen,

Ali,

Macioszek

2023

AoT

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In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents.

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Enhancing BNN structure learning of hybrid modeling strategy for free formulated mechanism complex systems

Cited by 3 publications

References 31 publications

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

Multi-view graph convolutional network with comprehensive structural learning: Enhancing dynamics representation for industrial processes

Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

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