Experimental Investigation on the Effect of Angles of Attack to the Flutter Speed of a Flat Plate in Axial Flow

Rahtika, I Putu Gede Sopan; Suarta, I Made; Rusmariadi, I Komang; Sunu, Putu Wijaya

doi:10.31940/logic.v21i2.2630

Cited by 1 publication

(2 citation statements)

References 15 publications

(18 reference statements)

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“…By continuously modifying the weights, the optimal solution problem is solved, and the loss of the function is minimised in accordance with the trend of the slope, resulting in the minimum loss value of the function. Because only one sample is randomly chosen and updated each time rather than all samples, SGD drastically reduces the computational complexity and saves a lot of time [ 14 ]. SGD is favoured by many researchers because of its easy convergence and fast training speed, and it has become their most commonly used optimization algorithm.…”

Section: Deconstruction Methods Of Pan-logical Probabilistic Algorith...mentioning

confidence: 99%

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Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks

Liu

2022

Computational Intelligence and Neuroscience

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A brand-new kind of flexible logic system called universal logic aims to address a variety of uncertain problems. In this study, the role of convolutional neural networks in assessing probabilistic pan-logic algorithms is investigated. A generic logic probability algorithm analysis based on a convolutional neural network is suggested due to the unpredictable outputs of the probabilistic algorithm and the difficulty of its analysis. The stochastic gradient descent technique and the error backpropagation algorithm are used to investigate the broad logic probability algorithm (SGD). The experimental data presented in this research show that the BP algorithm of the convolutional neural network has an accuracy rate of 89 percent when analysing the experimental data. As there are more experimental iterations, the error will go down. The SGD method proves that raising the algorithm’s learning rate reduces the loss value of the function. The loss value can be as low as 100%, and the algorithm analysis is closer to the real.

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Section: Deconstruction Methods Of Pan-logical Probabilistic Algorith...mentioning

confidence: 99%

“…In formula (14), y j represents the sample value of the jth sample and h(ω) represents the loss function. m represents the total number of iterations of the formula for calculation.…”

Section: Application Methods Of Sgd Algorithm In the Analysis Of The ...mentioning

confidence: 99%

Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Experimental Investigation on the Effect of Angles of Attack to the Flutter Speed of a Flat Plate in Axial Flow

Cited by 1 publication

References 15 publications

Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks

Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks

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