期刊论文

Research and application of sports physiological data prediction algorithm based on particle swarm optimization long short-term memory

中文

生物医学工程研究

1672-6278

2023

42

01

30-35+42

10.19529/j.cnki.1672-6278.2023.01.05

湖南中医药大学电子科学与技术学科开放基金资助项目(2018DK05)。

本校为第一机构

为提高现有智能运动产品对运动生理数据的预测效果,本研究提出了基于粒子群(particle swarm optimization, PSO)优化长短期记忆(long short-term memory, LSTM)网络的运...展开更多 为提高现有智能运动产品对运动生理数据的预测效果,本研究提出了基于粒子群(particle swarm optimization, PSO)优化长短期记忆(long short-term memory, LSTM)网络的运动生理数据预测模型。为使模型的网络拓扑结构与运动生理数据更加匹配,本研究利用传感器采集到的运动生理数据分别构建优化前后的模型,通过比较各模型的预测结果,评价其优化效果。结果显示,优化后模型预测结果的均方根误差(root mean square error,...

In order to improve the prediction effect of existing intelligent sports products on sports physiological data,we proposed a sports physiological data prediction model based on particle swarm optimization(PSO)long short-term memory(LSTM)network.To make the network topol...MORE In order to improve the prediction effect of existing intelligent sports products on sports physiological data,we proposed a sports physiological data prediction model based on particle swarm optimization(PSO)long short-term memory(LSTM)network.To make the network topology of the model batter match with the sports physio...