Design and analysis of three nonlinearly activated ZNN models for solving time-varying linear matrix inequalities in finite time

NEUROCOMPUTING

卷:390

Article

Zeng, Yuejie¹  Xiao, Lin²  Li, Kenli¹  Li, Jichun³  Li, Keqin¹  Jian, Zhen¹ 

[1] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Peoples R China

[2] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Changsha 410081, Peoples R China

[3] Teesside Univ, Sch Sci Engn & Design, Middlesbrough TS1 3BX, Cleveland, England

关键词: Zeroing neural network (ZNN);    Time-varying linear matrix inequalities;    Finite-time convergence;    Vectorization technique;    Sign-bi-power activation function;   

DOI  :  10.1016/j.neucom.2020.01.070

来源: Elsevier

【 摘 要 】

To obtain the superiority property of solving time-varying linear matrix inequalities (LMIs), three novel finite-time convergence zeroing neural network (FTCZNN) models are designed and analyzed in this paper. First, to make the Matlab toolbox calculation processing more conveniently, the matrix vectorization technique is used to transform matrix-valued FTCZNN models into vector-valued FTCZNN models. Then, considering the importance of nonlinear activation functions on the conventional zeroing neural network (ZNN), the sign-bi-power activation function (AF), the improved sign-bi-power AF and the tunable signbi-power AF are explored to establish the FTCZNN models. Theoretical analysis shows that the FTCZNN models not only can accelerate the convergence speed, but also can achieve finite-time convergence. Computer numerical results ulteriorly confirm the effectiveness and advantages of the FTCZNN models for finding the solution set of time-varying LMIs. (C) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

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