A Robust Predefined-Time Convergence Zeroing Neural Network for Dynamic Matrix Inversion

Zhu, Jingcan; Zhao, Liang; Chen, Lei; Chen, Long; Gong, Jianqiang

doi:10.1109/tcyb.2022.3179312

Cited by 39 publications

(4 citation statements)

References 48 publications

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“…Experiments [5,6] showed that the nervous system exhibited high synchronization during seizures. In order to better understand the structure of the brain, researchers have studied a large number of dynamic characteristics of neural networks [7][8][9][10][11][12]. However, continuous neuron models have the characteristics of high dimension and multiscale, which are not conducive to the study of complex and large-scale neural networks.…”

Section: Introductionmentioning

confidence: 99%

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

et al. 2023

Fractal Fract

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In order to enrich the dynamic behaviors of discrete neuron models and more effectively mimic biological neural networks, this paper proposes a bistable locally active discrete memristor (LADM) model to mimic synapses. We explored the dynamic behaviors of neural networks by introducing the LADM into two identical Rulkov neurons. Based on numerical simulation, the neural network manifested multistability and new firing behaviors under different system parameters and initial values. In addition, the phase synchronization between the neurons was explored. Additionally, it is worth mentioning that the Rulkov neurons showed synchronization transition behavior; that is, anti-phase synchronization changed to in-phase synchronization with the change in the coupling strength. In particular, the anti-phase synchronization of different firing patterns in the neural network was investigated. This can characterize the different firing behaviors of coupled homogeneous neurons in the different functional areas of the brain, which is helpful to understand the formation of functional areas. This paper has a potential research value and lays the foundation for biological neuron experiments and neuron-based engineering applications.

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Section: Introductionmentioning

confidence: 99%

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

et al. 2023

Fractal Fract

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“…At present, neural network is a hot topic in artificial intelligence, neurophysiology, nonlinear dynamics and other related fields. [1][2][3][4] It is widely used and has a good development prospect in many fields such as data processing, signal detection, and feature recognition. Neurons are the basic units of neural networks.…”

Section: Introductionmentioning

confidence: 99%

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

XiaoHua

Yang

et al. 2023

Chinese Phys. B

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At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use a LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

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“…In recent years, with the continuous improvement and development of deep learning [26][27][28][29][30], neural networks [31][32][33][34][35][36] have become an efficient solution for various time-varying problems. For example, Jin et al build the interference-tolerant fast convergence zeroing neural network (ITFCZNN) model [37] based on a new activation function, which exhibit excellent timevarying Robustness and convergence.…”

Section: Introductionmentioning

confidence: 99%

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

Lan

Liu²

2023

Front. Phys.

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As a powerful method for time-varying problems solving, the zeroing neural network (ZNN) is widely applied in many practical applications that can be modeled as time-varying linear matrix equations (TVLME). Generally, existing ZNN models solve these TVLME problems in the ideal no noise situation without inequality constraints, but the TVLME with noises and inequality constraints are rarely considered. Therefore, a non-linear activation function is designed, and based on the non-linear activation function, a non-linearly activated ZNN (NAZNN) model is proposed for solving constrained TVLME (CTVLME) problems. The convergence and robustness of the proposed NAZNN model are verified theoretically, and simulation results further demonstrate the effectiveness and superiority of the NAZNN model in dealing with CTVLME and the constrained robot manipulator trajectory tracking problems. In addition, the wheeled robot trajectory tracking fault problems with physical constraints are also analyzed theoretically, and the proposed NAZNN model is also applied to the manipulator trajectory tracking fault problem, and the experimental results prove that the NAZNN model also deal with the manipulator trajectory tracking fault problem effectively.

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A Robust Predefined-Time Convergence Zeroing Neural Network for Dynamic Matrix Inversion

Cited by 39 publications

References 48 publications

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

Towards non-linearly activated ZNN model for constrained manipulator trajectory tracking

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