Long short-term memory(LSTM) is an effective solution to time sequence prediction. Considering the data perturbations, in this letter, a variant model of LSTM is proposed to achieve robustness of prediction. Specifically, data processing procedure in the recurrent unit of proposed model is reformulated, the gates are controlled by only one variable, and the variable is the sum of long-term memory and the current input. Due to the simplified two-gate structure of proposed model, the speed of prediction is improved as well. The experiments on three datasets verify that the proposed model with simplified structure has higher robustness and shorter running time than the traditional LSTM model.

长短期记忆（LSTM）是时序预测的有效解决方案。考虑到数据扰动，在本文中，提出了LSTM的变体模型以实现预测的鲁棒性。具体而言，对提出的模型的循环单元中的数据处理过程进行了重新制定，门仅由一个变量控制，并且该变量是长期内存和当前输入的总和。由于所提出模型的简化的两门结构，预测速度也得到了提高。在三个数据集上的实验证明，与传统的LSTM模型相比，所提出的结构简化的模型具有更高的鲁棒性和更短的运行时间。