Functional echo state network（FESN） is a new kind of recurrent neural network which has been successfully used for time series classification. In order to make FESN more suitable for multi-variable time series data classification task, we present a novel FESN model by modifying the output layer of original FESN with softmax regression, and the L-BFGS algorithm is employed to train such proposed model. Moreover, the genetic algorithm is used to determine the hyper-parameter of the improved FESN. The experimental results show that the proposed approach can achieve better accuracy than classical classifiers such as support vector machine, Long Short-Term Memory neural network and original FESN, in the context of multi-variable series data classification.
类型: 国际会议
作者: Jian-xi YANG,Ying-ying HE,Zheng-wu LI,Ren LI,Jing-pei DAN
来源: 2019 International Conference on Information Technology, Electrical and Electronic Engineering (ITEEE 2019) 2019-01-20
年度: 2019
分类: 基础科学,信息科技
专业: 数学,自动化技术
单位: College of Information Science and Engineering,Chongqing Jiaotong University,Highway Administration Bureau of Ningxia Hui Autonomous Region,College of Computer Science,Chongqing University
分类号: TP18;O211.61
DOI: 10.26914/c.cnkihy.2019.078503
页码: 494-499
总页数: 6
文件大小: 825k
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