Cell-expanded Long Short-term Memory

被引：0

作者：

Rokui, Jun ^{[1
]}

Adachi, Rin ^{[2
]}

机构：

[1] Univ Shizuoka, Grad Sch Management & Informat Innovat, Suruga Ku, 52-1 Yada, Shizuoka 4228526, Japan

[2] MEDIA SEEK INC, Minato Ku, Daiwaazabuterasu 3-20-1, Tokyo 1060047, Japan

来源：

2022 JOINT 12TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS AND 23RD INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (SCIS&ISIS) | 2022年

关键词：

D O I：

10.1109/SCISISIS55246.2022.10001924

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Long short-term memory (LSTM) is a recurrent neural network (RNN) that is widely used for historical time series prediction. LSTM has a memory architecture called a memory cell, which enables long-term time-series prediction. Many historical time series have complex changes; thus, a single memory cell cannot fully capture the features. In this paper, we propose a method that exhibits good predictive performance against complex time-series changes by incorporating additional memory cells.

引用

页数：6

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