Effective condition number for the selection of the RBF shape parameter with the fictitious point method

Noorizadegan, Amir; Chen, Chuin-Shan; Young, D. L.; Chen, C.S.

doi:10.1016/j.apnum.2022.04.003

Cited by 19 publications

(1 citation statement)

References 16 publications

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“…The first subgraph represents the initial distribution of sample points, the second subgraph represents placing a basis function on each sample, and the third subgraph represents the constructed RBF interpolation function curve. [ 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Media Teaching and Effectiveness Evaluation Integrating Big Data and Artificial Intelligence

Zhang

2022

Computational Intelligence and Neuroscience

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With the development of digital media technology, its application in teaching and learning is becoming more widespread. Digital media technology helps present information in transmitting knowledge or skills, reduces cognitive load, and promotes understanding of knowledge. Evaluation of the effectiveness of digital media teaching has also become important. A scientific and reasonable evaluation of digital media teaching effectiveness can help teachers select digital media technology and grasp the amount, degree, and timing of digital media use to change teaching effectiveness. This paper proposed using a combination of big data and artificial intelligence methods to evaluate the effectiveness of digital media teaching methods using the RBF neural network model. The digital media teaching effectiveness evaluation was used as the input variable of RBF, the degree of digital media effectiveness was the output variable and the neural network was trained through the collected sample data. The research results showed that the RBF neural network model proposed in this paper has a strong generalization and extension ability in evaluating digital media teaching effectiveness, providing a new way to evaluate digital media teaching effectiveness.

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Section: Literature Reviewmentioning

confidence: 99%