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Growing Echo State Network With an Inverse-Free Weight Update Strategy

文献类型：期刊论文

作者Chen, Xiufang1; Luo, Xin2; Jin, Long1; Li, Shuai1; Liu, Mei1
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2022-03-22
页码12
关键词Reservoirs Training Computational modeling Neurons Topology Standards Numerical models Echo state network (ESN) inverse-free algorithm incremental scheme Schur complement Sherman-Morrison formula
ISSN号2168-2267
DOI10.1109/TCYB.2022.3155901
通讯作者Jin, Long(jinlongsysu@foxmail.com) ; Li, Shuai(lishuai@lzu.edu.cn)
英文摘要An echo state network (ESN) draws widespread attention and is applied in many scenarios. As the most typical approach for solving the ESN, the matrix inverse operation of high computational complexity is involved. However, in the modern big data era, addressing the heavy computational burden problem is necessary. In order to reduce the computational load, an inverse-free ESN (IFESN) is proposed for the first time in this article. Besides, an incremental IFESN is constructed to attain the network topology with theoretical proof on the training error's monotone decline property. Simulations and experiments are conducted on several numerical and real-world time-series benchmarks, and corresponding results indicate that the proposed model is superior to some existing models and possesses excellent practical application potential. The source code is publicly available at https://github.com/LongJin-lab/the-supplementary-file-for-CYB-E-2021-04-0944.
资助项目National Natural Science Foundation of China[62176109] ; Natural Science Foundation of Gansu Province[21JR7RA531] ; Team Project of Natural Science Foundation of Qinghai Province China[2020-ZJ-903] ; Gansu Provincial Youth Doctoral Fund of Colleges and Universities[2021QB-003] ; Fundamental Research Funds for the Central Universities[lzujbky-2021-65] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Supercomputing Center of Lanzhou University ; CAAI Huawei MindSpore Open Fund[CAAIXSJLJJ-2021035A] ; Special Projects of the Central Government in Guidance of Local Science and Technology Development[YDZX20216200001297] ; Science and Technology Project of Chengguan District of Lanzhou[2021JSCX0014]
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000777339900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/15503]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Jin, Long; Li, Shuai
作者单位1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Xiufang,Luo, Xin,Jin, Long,et al. Growing Echo State Network With an Inverse-Free Weight Update Strategy[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022:12.
APA Chen, Xiufang,Luo, Xin,Jin, Long,Li, Shuai,&Liu, Mei.(2022).Growing Echo State Network With an Inverse-Free Weight Update Strategy.IEEE TRANSACTIONS ON CYBERNETICS,12.
MLA Chen, Xiufang,et al."Growing Echo State Network With an Inverse-Free Weight Update Strategy".IEEE TRANSACTIONS ON CYBERNETICS (2022):12.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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