<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, a custom recurrent neural network is constructed based on RNN. The internal structure of the GRU recurrent neural is mainly analyzed, and the activation function is used to ensure the smooth flow of information in the backward propagation of the neural network. By analyzing the recurrent neural in the processing sequence as well as correlation data, the time-based backpropagation algorithm is constructed. Take advantage of Highway connection in backward propagation to improve the computational speed of the improved recurrent neural network. The moving average method is used to deal with abnormal data and combined with the SGD algorithm to avoid the problem of using all training samples in one iteration so as to establish the optimal model of teacher training time series. The results show that: 48% of the training target factors account for teacher training mainly focused on professional knowledge and 35% on practical ability.</p>
</abstract>

In this paper, a custom recurrent neural network is constructed based on RNN. The internal structure of the GRU recurrent neural is mainly analyzed, and the activation function is used to ensure the smooth flow of information in the backward propagation of the neural network. By analyzing the recurrent neural in the processing sequence as well as correlation data, the time-based backpropagation algorithm is constructed. Take advantage of Highway connection in backward propagation to improve the computational speed of the improved recurrent neural network. The moving average method is used to deal with abnormal data and combined with the SGD algorithm to avoid the problem of using all training samples in one iteration so as to establish the optimal model of teacher training time series. The results show that: 48% of the training target factors account for teacher training mainly focused on professional knowledge and 35% on practical ability.

The construction of a teacher training system for artificial intelligence professionals in universities oriented to RNN models

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, a custom recurrent neural network is constructed based on RNN. The internal structure of the GRU recurrent neural is mainly analyzed, and the...

The construction of a teacher training system for artificial intelligence professionals in universities oriented to RNN models

Data publikacji: 15 paź 2023

Zakres stron: -

Otrzymano: 12 gru 2022

Przyjęty: 25 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00656

Słowa kluczowe
RNN, GRU, Backpropagation algorithm, SGD algorithm, Activation function

© 2023 Lintao Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The construction of a teacher training system for artificial intelligence professionals in universities oriented to RNN models

Data publikacji: 15 paź 2023

Zakres stron: -

Otrzymano: 12 gru 2022

Przyjęty: 25 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00656

Słowa kluczoweRNN, GRU, Backpropagation algorithm, SGD algorithm, Activation function

© 2023 Lintao Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
RNN, GRU, Backpropagation algorithm, SGD algorithm, Activation function