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- Title
Dynamic analysis and hardware implementation of multi-scroll Hopfield neural networks with three different memristor synapses.
- Authors
Yu, Fei; Wu, Chaoran; Lin, Yue; He, Shaoqi; Yao, Wei; Cai, Shuo; Jin, Jie
- Abstract
Neurons play an important role in forming behaviors and cognition through synaptic interactions. When organized into neural networks, these neurons can exhibit complex dynamic behaviors, such as multi-scroll and hyperchaotic attractors. In this study, we investigated the dynamic behavior of memristor synapses between four small neurons and proposed a new segmented linear memristor. Based on this, an improved version of the second memristor was obtained by modification. Subsequently, a new neural network was constructed, and the coefficients of the neural network were slightly adjusted. Two memristor synapse HNNs were constructed separately. Finally, these memristors were integrated as synapses and autapses to create the third memristive synapse HNN. It is worth noting that the last memristive HNN can generate hyperchaotic multi-scroll attractors, and its memristor function effectively controls the number of scroll axes. The dynamics of the hyperchaotic multi-scroll memristive HNN were analyzed using phase diagrams, bifurcation diagrams, Poincare maps, and Lyapunov exponent plots. In terms of hardware implementation, we used FPGA to implement the proposed hyperchaotic multi-scroll model and demonstrated 6 memristive HNN attractors on an oscilloscope to validate the accuracy of the model. Finally, a simple image encryption scheme with high information entropy was designed, demonstrating good encryption effectiveness.
- Subjects
HOPFIELD networks; POINCARE maps (Mathematics); IMAGE encryption; LYAPUNOV exponents; BIFURCATION diagrams; SYNAPSES
- Publication
Nonlinear Dynamics, 2024, Vol 112, Issue 14, p12393
- ISSN
0924-090X
- Publication type
Article
- DOI
10.1007/s11071-024-09614-8