ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||6|
|Section||Algorithm Optimization and Application|
|Published online||23 June 2022|
Research on recurrent neural network model based on weight activity evaluation
1 School of Information Engineering, Wuhan Institute of Design and Sciences, Wuhan, Hubei, China
2 School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei, China
* Corresponding author: firstname.lastname@example.org
Given the complex structure and parameter redundancy of recurrent neural networks such as LSTM, related research and analysis on the structure of recurrent neural networks have been done. To improve the structural rationality of the recurrent neural network and reduce the amount of calculation of network parameters, a weight activity evaluation algorithm is proposed that evaluates the activity of the basic unit of the network. Through experiments and tests on arrhythmia data, the differences in the weight activity of the LSTM network and the change characteristics of weights and gradients are analyzed. The experimental results show that this algorithm can better optimize the recurrent neural network structure and reduce the redundancy of network parameters.
Key words: Weight activity evaluation / Parameter redundancy / Recurrent neural network
© The Authors, published by EDP Sciences, 2022
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