Training neural-network-based controller on distributed machine learning platform for power electronics systems

Yang, Yi; Chung, Henry Shu-Hung; Cheng, R.S.; Leung, C. M.; Zhan, Xiaoqing; Lo, W.L.; Kwok, James T.; Zhang, Mengjie

doi:10.1109/ecce.2017.8096563

Cited by 17 publications

(16 citation statements)

References 8 publications

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“…The diagrammatic representation implies the conditional dependence relationship between the decision variables. The underlying relationship in the model is formulated in the Bayesian framework [1] and can be inferred in Design [79], [80], Control [4], [51], [81]- [85], Maintenance [86]- [97] Radial Control [121], [122], Maintenance [7], [74], [96], [118], [123]- [126] Relevance vector machine (RVM)…”

Section: Machine Learningmentioning

confidence: 99%

“…A generic method is to start with a relatively small number of neurons and then gradually increase it according to the training error. For the activation function in the hidden layer, there are various options, including sigmoid [4], [51], [52], [83], radial basis function [50], [150], hyperbolic tangent function [106], [151], wavelet [46], [53], [84], [152], etc. It is worth mentioning that the wavelet activation function possesses the superior capabilities of convergence speed and generalization.…”

Section: B Neural Network-based Controllermentioning

confidence: 99%

“…4) Training Methods of Neural Network: Essentially, the training of the neural network is an optimization task. Of course, it can be completed with conventional optimization methods, e.g., PSO [51], recursive least square [99], Kalman filter [105], etc. Considering a large number of parameters in the neural network, these conventional optimization methods are generally inefficient.…”

Section: B Neural Network-based Controllermentioning

confidence: 99%

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An Overview of Artificial Intelligence Applications for Power Electronics

Zhao

Blaabjerg

Wang

2021

IEEE Trans. Power Electron.

507

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This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics. This paper is accompanied by an Excel file listing the relevant publications for statistical analytics.

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Section: Machine Learningmentioning

confidence: 99%

Section: B Neural Network-based Controllermentioning

confidence: 99%

Section: B Neural Network-based Controllermentioning

confidence: 99%

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An Overview of Artificial Intelligence Applications for Power Electronics

Zhao

Blaabjerg

Wang

2021

IEEE Trans. Power Electron.

507

View full text Add to dashboard Cite

show abstract

“…A generic method is to start with a relatively small number of neurons and then gradually increase it according to the training error. For the activation function in the hidden layer, there are various options, including sigmoid [4,51,52,83], radial basis function [50,149], hyperbolic tangent function [105,150], wavelet [46,53,84,151], etc. It is worth mentioning that the wavelet activation function possesses the superior capabilities of convergence speed and generalization.…”

Section: Input Layer Hidden Layer Output Layermentioning

confidence: 99%

“…Of course, it can be completed with conventional optimization methods, e.g. PSO [51], recursive least square [98], Kalman filter [104], etc. Considering a large number of parameters in the neural network, these conventional optimization methods are generally inefficient.…”

Section: Input Layer Hidden Layer Output Layermentioning

confidence: 99%

An overview of artificial intelligence applications for power electronics

Blaabjerg¹,

Wang²

2020

Preprint

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<div>This is a preprint version of the manuscript submitted to IEEE on June 4, 2020.</div><div><br></div><div>This paper gives an overview of the Artificial Intelligence (AI) applications for power electronic systems. The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.<br></div>

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Design of Interleaved DC-DC SEPIC Converter Based Switching Regulator Using Neural Network Based Machine Learning Algorithm

M.G,

2023

2023 IEEE World Conference on Applied Intelligence and Computing (AIC)

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Training neural-network-based controller on distributed machine learning platform for power electronics systems

Cited by 17 publications

References 8 publications

An Overview of Artificial Intelligence Applications for Power Electronics

An Overview of Artificial Intelligence Applications for Power Electronics

An overview of artificial intelligence applications for power electronics

Design of Interleaved DC-DC SEPIC Converter Based Switching Regulator Using Neural Network Based Machine Learning Algorithm

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