基于演化LSTM神经网络的用户终端睡眠预测模型

用户处于睡眠状态时手机后台自主挂起不必要的系统或应用进程可以有效降低能耗, 因此在不损害用户使用体验的前提下准确判断用户是否处于睡眠状态具有重要意义. 围绕该问题设计了覆盖率和唤醒率作为新的衡量指标, 提出一种基于LSTM神经网络的睡眠预测模型, 结合LSTM神经网络能够较好处理时序特征数据的特点和演化算法能够优化不可导目标函数的特性, 将LSTM神经网络的参数作为差分演化算法的优化参数, 覆盖率和唤醒率的综合目标作为选择函数, 同时在每次迭代中重新评估选择函数使其适应小批量训练法. 实验结果表明, 采用演化算法训练LSTM神经网络得到的预测结果相较于传统分类模型能在低唤醒率时达到更好的覆盖率, 平均提升约5%.

Evolutionary LSTM Neural Network as Sleep Prediction Model on Smartphone

Suspending unnecessary system or application processes in the background of the mobile phone while the user is sleeping can effectively reduce energy consumption, so it is of great significance to accurately determine whether the user is sleeping without compromising the user experience. Based on this problem, the coverage rate and wake rate are designed as new metrics. A sleep prediction model based on LSTM neural network is proposed, the LSTM neural network can handle time-series feature data and the evolution algorithm can optimize non derivable optimization targets. The parameters of the LSTM neural network are used as the optimization parameters of the differential evolution algorithm, and the comprehensive target of coverage and wake-up rates are used as the selection function. The selection function is re-evaluated in each iteration to use the mini-batch training. The experimental results show that compared with the traditional classification model, the prediction results obtained by training the LSTM neural network with evolutionary algorithm can achieve better coverage at low wake-up rate, with an average improvement of about 5%.