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Dynamics Analysis and Hardware Implementation of Multi-Scroll Hyperchaotic Hidden Attractors Based on Locally Active Memristive Hopfield Neural Network

NONLINEAR DYNAMICS(2024)

Hunan University | Changsha University of Science and Technology

Cited 16|Views2
Abstract
It is believed that local activation is the origin of all complexities, and the locally active memristive synaptic neural network can generate complex chaotic dynamic behaviors, such as hyperchaotic, multi-scroll, multi-stability and hidden dynamical behaviors. However, there are few studies on the simultaneous occurrence of multiple complex dynamic behaviors in neural networks. No chaotic system of multi-scroll hyperchaotic hidden attractors based on neural network has been found yet. To solve the problem, in this paper, we propose a new locally active memristive Hopfield neural network (HNN) model based on a multi-segment function, which is affected by electromagnetic radiation and external current. The multi-scroll hyperchaotic hidden attractors are found in the memristive HNN for the first time. Theoretical analysis and numerical simulation results show that the memristive HNN model has no equilibrium point, and the number of multi-scroll attractors is controlled by the state equation parameters of the memristive synapse. In addition, the structures and number of scrolls are also affected by electromagnetic radiation and external current. At the same time, under the appropriate parameter conditions, by modifying the initial value of the system, the memristive HNN has a controllable number of coexisting attractors, showing extreme multi-stability. Finally, a memristive HNN analog circuit is designed. The hardware experiment results reproduce the multi-scroll dynamics phenomenon, which verifies the correctness of the theoretical analysis and numerical simulation.
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Key words
Locally active memristor,Hopfield neural network,Multi-scroll hyperchaotic hidden attractors,External stimulus,Hardware implementation
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要点】:本文首次提出了一种基于多段函数的局部激活忆阻Hopfield神经网络模型,发现了多涡卷超混沌隐藏吸引子,并实现了硬件验证。

方法】:通过构建新的忆阻Hopfield神经网络模型,并利用多段函数实现局部激活,从而产生复杂的多涡卷超混沌隐藏动态行为。

实验】:设计了一种忆阻Hopfield神经网络模拟电路,使用电磁辐射和外部电流作为参数,实验结果重现了多涡卷动态现象,验证了理论分析和数值模拟的正确性。数据集名称未在文中提及。