A novel hybrid Zhang neural network model for time-varying matrix inversion

Sowmya, G; Thangavel, P.; Shankar, Varun

doi:10.1016/j.jestch.2021.05.013

Cited by 5 publications

(7 citation statements)

References 27 publications

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“…The dotted blue tricuspid valve is the desired path, and the red curve is the tracking trajectory of the manipulator. 23c and 24c, it can be seen that the tracking errors of ANPVZNN-based model (39) are much smaller than the OZNN-based model (40) in noisy environment, which further demonstrates its accuracy and robustness.…”

Section: Application To Robotic Manipulatormentioning

confidence: 85%

“…Figures 23c and 24c are the tracking errors of the ANPVZNN-based and OZNN-based mobile manipulator. From Figures23c and 24c, it can be seen that the tracking errors of ANPVZNN-based model(39) are much smaller than the OZNN-based model(40) in noisy environment, which further demonstrates its accuracy and robustness.To further validate the accurateness and practicability of the ANPVZNN in manipulator trajectory tracking application, the physical experiments are conducted with IRB120 robot manipulator, and the experiment results are presented in Figure25. The dotted blue tricuspid valve is the desired path, and the red curve is the tracking trajectory of the manipulator.…”

mentioning

confidence: 83%

“…ϕ(•) in Equations ( 39) and ( 40) is the proposed AF in Equation ( 12) and SBPAF in Equation ( 9), respectively. The desired path is a tricuspid valve, and the ANPVZNN-based model (39) and OZNN-based model (40) are applied to control the mobile manipulator to track the desired tricuspid valve path in periodic noise cos(t) polluted environment, and the simulated results are presented in Figures 23 and 24.…”

Section: Application To Robotic Manipulatormentioning

confidence: 99%

“…Moreover, considering the noises, in reality, the robustness of the dynamic model should also be taken into account. In reference [40], an HZNN model is designed to solve time-varying matrix inversion, and it has fast convergent speed than the GNN and ZNN model. In reference [41], a varying parameter recurrent neural network is proposed for solving time-varying matrix inversion, and it has accelerated convergence time.…”

Section: Introductionmentioning

confidence: 99%

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Design and Analysis of Anti-Noise Parameter-Variable Zeroing Neural Network for Dynamic Complex Matrix Inversion and Manipulator Trajectory Tracking

Zhou

Tan

2022

Electronics

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Dynamic complex matrix inversion (DCMI) problems frequently arise in the territories of mathematics and engineering, and various recurrent neural network (RNN) models have been reported to effectively find the solutions of the DCMI problems. However, most of the reported works concentrated on solving DCMI problems in ideal no noise environment, and the inevitable noises in reality are not considered. To enhance the robustness of the existing models, an anti-noise parameter-variable zeroing neural network (ANPVZNN) is proposed by introducing a novel activation function (NAF). Both of mathematical analysis and numerical simulation results demonstrate that the proposed ANPVZNN model possesses fixed-time convergence and robustness for solving DCMI problems. Besides, a successful ANPVZNN-based manipulator trajectory tracking example further verifies its robustness and effectiveness in practical applications.

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Section: Application To Robotic Manipulatormentioning

confidence: 85%

mentioning

confidence: 83%

Section: Application To Robotic Manipulatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Analysis of Anti-Noise Parameter-Variable Zeroing Neural Network for Dynamic Complex Matrix Inversion and Manipulator Trajectory Tracking

Zhou

Tan

2022

Electronics

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“…In the last decades, the zeroing neural dynamics (ZND) model originated from the Hopfield neural network has been widely investigated and applied [12]- [14]. It is endowed with predictive computing due to the utilization of time derivative of time-variant parameters, which can effectively Model in [8] Model in [17] Model in [10] Model in [12] Model in [21] Model in [22] This work Adaptive parameter…”

Section: Introductionmentioning

confidence: 99%

Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

Zhuang

Zou

et al. 2023

IEEE Access

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The time-variant Lyapunov equation (TVLE) has played an important role in many fields due to its ubiquity and many neural dynamics models have been developed to obtain the online solution of the TVLE. In prevalent methods, the gradient neural dynamics (GND) models suffer from the large residual error due to the lack of predictive computing, while the zero neural dynamics (ZND) models have large computing complexity because of the inverse of the mass matrix in models. To mitigate these deficiencies, an adaptive parameter containing the time-derivative of time-variant parameters in the TVLE is added to the GND model to form the adaptive GND (AGND) model, which enables the AGND model predictive computing as ZND models and inherits the free of matrix inverse from the GND models. Moreover, two strategies are proposed to design the accelerated AGND (AAGND) models that enjoy a faster convergence rate. The accuracy and the convergence rate of AAGND models are theoretically analyzed, indicating that AAGND models achieve zero residual error and a faster convergence rate. In addition, numerical simulations and two applications are provided to verify the theoretical analyses and the efficiency of AAGND models. The experimental results demonstrate that the AAGND model can solve the TVLE with high accuracy and have great potentialities in applications.

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A novel extended Li zeroing neural network for matrix inversion

Gerontitis

Stanimirović

et al. 2023

Neural Comput & Applic

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A novel hybrid Zhang neural network model for time-varying matrix inversion

Cited by 5 publications

References 27 publications

Design and Analysis of Anti-Noise Parameter-Variable Zeroing Neural Network for Dynamic Complex Matrix Inversion and Manipulator Trajectory Tracking

Design and Analysis of Anti-Noise Parameter-Variable Zeroing Neural Network for Dynamic Complex Matrix Inversion and Manipulator Trajectory Tracking

Accelerated Adaptive Gradient Neural Dynamics Models for Solving Time-Variant Lyapunov Equation and Their Applications

A novel extended Li zeroing neural network for matrix inversion

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