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A new super-predefined-time convergence and noise-tolerant RNN for solving time-variant linear matrix-vector inequality in noisy environment and its application to robot arm

文献类型：期刊论文

作者Zheng, Boyu1; Yue, Chong1; Wang, Qianqian1; Li, Chunquan1; Zhang, Zhijun2; Yu, Junzhi3,4; Liu, Peter X.5
刊名NEURAL COMPUTING & APPLICATIONS
出版日期2023-12-20
页码17
ISSN号0941-0643
关键词Linear matrix-vector inequality Recurrent neural network Robustness Time-variant problem Convergence
DOI10.1007/s00521-023-09264-8
通讯作者Li, Chunquan(lichunquan@ncu.edu.cn)
英文摘要Recurrent neural networks (RNNs) are excellent solvers for time-variant linear matrix-vector inequality (TVLMVI). However, it is difficult for traditional RNNs to track the theoretical solution of TVLMVI under non-ideal conditions [e.g., noisy environment]. Therefore, by introducing a novel nonlinear activation function (NNAF) and time-variant-gain, a new super-predefined-time convergence and noise-tolerant RNN (SPCNT-RNN) is proposed to acquire an online solution to TVLMVI in noisy environment. The difference between SPCNT-RNN and traditional fixed-parameter RNNs (FP-RNNs) is that the error function equation of SPCNT-RNN has NNAF and time-variant-gain coefficient. Due to this difference, the SPCNT-RNN can achieve super-predefined-time convergence in both noise-free and noisy environments, which is superior to that of existing RNNs. The stability, super-predefined-time convergence, and robustness of SPCNT-RNN are theoretically demonstrated. Moreover, the simulation results between various existing RNNs and SPCNT-RNN verify the feasibility, validity, robustness and rapid convergence effect of the proposed SPCNT-RNN.
WOS关键词RECURRENT NEURAL-NETWORK ; NONLINEAR OPTIMIZATION ; ZNN MODEL ; DESIGN ; EQUALITY
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science
语种英语
出版者SPRINGER LONDON LTD
WOS记录号WOS:001127132800002
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/54862]  
专题复杂系统认知与决策实验室
通讯作者Li, Chunquan
作者单位1.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Peoples R China
2.South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
4.Peking Univ, Coll Engn, Dept Mech & Engn Sci, State Key Lab Turbulence & Complex Syst,BIC ESAT, Beijing, Peoples R China
5.Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON K1S 5B6, Canada
推荐引用方式
GB/T 7714		 Zheng, Boyu,Yue, Chong,Wang, Qianqian,et al. A new super-predefined-time convergence and noise-tolerant RNN for solving time-variant linear matrix-vector inequality in noisy environment and its application to robot arm[J]. NEURAL COMPUTING & APPLICATIONS,2023:17.
APA Zheng, Boyu.,Yue, Chong.,Wang, Qianqian.,Li, Chunquan.,Zhang, Zhijun.,...&Liu, Peter X..(2023).A new super-predefined-time convergence and noise-tolerant RNN for solving time-variant linear matrix-vector inequality in noisy environment and its application to robot arm.NEURAL COMPUTING & APPLICATIONS,17.
MLA Zheng, Boyu,et al."A new super-predefined-time convergence and noise-tolerant RNN for solving time-variant linear matrix-vector inequality in noisy environment and its application to robot arm".NEURAL COMPUTING & APPLICATIONS (2023):17.

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来源：自动化研究所

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