A Multivariable Time Series Classification Approach Based on Improved Functional Echo State Network

A Multivariable Time Series Classification Approach Based on Improved Functional Echo State Network

论文摘要

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.

论文目录

文章来源

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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A Multivariable Time Series Classification Approach Based on Improved Functional Echo State Network
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