A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion. (September 2019)

• Record Type: Journal Article
• Authors: Xiao, Lin; Zhang, Yongsheng; Dai, Jianhua; Chen, Ke; Yang, Song; Li, Weibing; Liao, Bolin; Ding, Lei; Li, Jichun
• Is Part Of: Neural networks. Volume 117(2019)
• Page Start: 124
• Page End: 134
• Publication Date: 2019-09
• Subjects: Zeroing neural network -- Recurrent neural network -- Time-dependent matrix inversion -- Noise tolerance -- Finite-time convergence; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.05.005
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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 JACO 2 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.