计算机与数字工程2025，Vol.53Issue(10)：2677-2682,2738,7.DOI:10.3969/j.issn.1672-9722.2025.10.001
基于马卡龙序列分解的Transformer支路参数辨识
Transformer Branch Parameter Identification Based on Macaron Sequence Decomposition
摘要
Abstract
Parameter identification plays an important role in power system.As a long-term prediction problem of time series,in order to explain the complex time pattern,a Transformer branch parameter identification method based on Macaron sequence de-composition is proposed.Among them,the sequence decomposition module is regarded as the internal block of the deep model.Dur-ing the whole prediction process,the hidden sequence is gradually decomposed,including the past sequence and the intermediate results of the prediction.At the same time,the Macaron network is used to replace the original feedforward layer in Transformer with two half-step feedforward layers,and the self-attention module and the sequence decomposition module are placed between them.The experimental results show that the proposed algorithm has higher prediction accuracy and is significantly better than other ma-chine learning algorithms and deep learning algorithms.关键词
马卡龙网络/序列分解/自注意力机制/参数辨识/深度学习Key words
Macaron network/sequence decomposition/self-attention mechanism/parameter identification/deep learning分类
信息技术与安全科学引用本文复制引用
WANG Linpeng,SONG Gongfei,WANG Menglong..基于马卡龙序列分解的Transformer支路参数辨识[J].计算机与数字工程,2025,53(10):2677-2682,2738,7.基金项目
国家自然科学基金项目(编号:61973170)资助. （编号:61973170）
