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

Zeng, Yuejie and Xiao, Lin and Li, Kenli and Li, Jichun and Li, Keqin and Jian, Zhen (2020) 'Design and analysis of three nonlinearly activated ZNN models for solving time-varying linear matrix inequalities in finite time.' Neurocomputing. ISSN 0925-2312

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Abstract

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 sign-bi-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.

Item Type: Article
Uncontrolled Keywords: Zeroing neural network (ZNN), Time-varying linear matrix inequalities, Finite-time convergence, Vectorization technique, Sign-bi-power activation function
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 15 Apr 2020 12:54
Last Modified: 23 Jan 2021 02:00
URI: http://repository.essex.ac.uk/id/eprint/27309

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