计算机应用与软件2024，Vol.41Issue(6)：296-304,319,10.DOI:10.3969/j.issn.1000-386x.2024.06.043
基于权重活性评价的循环神经网络模型
RECURRENT NEURAL NETWORK MODEL BASED ON WEIGHT ACTIVITY EVALUATION
张承 1郑明 1胡雨阳 1夏定纯1
作者信息
- 1. 武汉纺织大学数学与计算机学院 湖北武汉 430200
- 折叠
摘要
Abstract
Aimed at the complex structure and parameter redundancy of recurrent neural networks such as LSTM,the structure of recurrent neural networks is studied and analyzed.In order 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 and screens its structure.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 were analyzed.The experimental results show that this algorithm can better optimize the structure of the recurrent neural network and reduce the redundancy of network parameters.关键词
权重/活性/评价/参数冗余/循环神经网络Key words
Weight/Activity/Evaluation/Parameter redundancy/Recurrent neural network分类
信息技术与安全科学引用本文复制引用
张承,郑明,胡雨阳,夏定纯..基于权重活性评价的循环神经网络模型[J].计算机应用与软件,2024,41(6):296-304,319,10.
