Memristive-cyclic Hopfield neural network: spatial multi-scroll chaotic attractors and spatial initial-offset coexisting behaviors

被引：29

作者：

Bao, Han ^{[1
]}

Chen, Zhuguan ^{[1
]}

Chen, Mo ^{[1
]}

Xu, Quan ^{[1
]}

Bao, Bocheng ^{[1
]}

机构：

[1] Changzhou Univ, Sch Microelect & Control Engn, Changzhou 213159, Peoples R China

来源：

NONLINEAR DYNAMICS | 2023年 / 111卷 / 24期

关键词：

Memristor; Cyclic Hopfield neural network; Multi-scroll chaotic attractor; Initial-offset coexisting attractors; Initial condition; FPGA hardware platform; DYNAMICS ANALYSIS; COMPLEX DYNAMICS; IMPLEMENTATION; SYNAPSE;

D O I：

10.1007/s11071-023-08993-8

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Neural networks with changeable synaptic weights usually exhibit more complex and diverse dynamics than those with fixed synaptic weights. It was proved that the tri-neuron resistive-cyclic Hopfield neural network (RC-HNN) cannot show chaos. To this end, we first consider a RC-HNN with bipolar pulse current to generate double-scroll chaotic attractors. On this basis, we then construct a tri-neuron memristive-cyclic Hopfield neural network (MC-HNN) by replacing the resistive weights with memristive ones, and spatial multi-scroll chaotic behaviors and spatial initial-offset coexisting behaviors are revealed therein using phase portrait, Poincare map and basin of attraction. The results manifest that by setting the parameters related to the internal states of three memristors, the MC-HNN can not only generate spatial multi-scroll chaotic attractors (MSCAs) with different scroll numbers, but also produce spatial initial-offset coexisting attractors (IOCAs) with different attractor numbers. Besides, an FPGA hardware platform is developed and the spatial MSCAs and spatial IOCAs are displayed experimentally to confirm the numerical simulations.

引用

页码：22535 / 22550

页数：16