A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion

Lin Xiao, Yongsheng Zhang, Jianhua Dai, Ke Chen, Song Yang, Weibing Li, Bolin Liao, Lei Ding, Jichun Li

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In this work, a new zeroing neural network (ZNN) using a versatile activation function (VAF) is presented and introduced for solving time-dependent matrix inversion. Unlike existing ZNN models, the proposed ZNN model not only converges to zero within a predefined finite time but also tolerates several noises in solving the time-dependent matrix inversion, and thus called new noise-tolerant ZNN (NNTZNN) model. In addition, the convergence and robustness of this model are mathematically analyzed in detail. Two comparative numerical simulations with different dimensions are used to test the efficiency and superiority of the NNTZNN model to the previous ZNN models using other activation functions. In addition, two practical application examples (i.e., a mobile manipulator and a real Kinova JACO2 robot manipulator) are presented to validate the applicability and physical feasibility of the NNTZNN model in a noisy environment. Both simulative and experimental results demonstrate the effectiveness and tolerant-noise ability of the NNTZNN model.
Original languageEnglish
Pages (from-to)124-134
Number of pages11
JournalNeural Networks
Early online date15 May 2019
Publication statusPublished - 30 Sept 2019


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