https://doi.org/10.1140/epjs/s11734-024-01320-1
Regular Article
Complex dynamical analysis of a discrete memristive neural network and its DSP implementation
1
School of Information Science and Engineering, Dalian Polytechnic University, 116034, Dalian, China
2
School of Management, Dalian Polytechnic University, 116034, Dalian, China
c
sunbo_0709@126.com
d
moujun@dlpu.edu.cn
Received:
19
February
2024
Accepted:
29
August
2024
Published online:
5
September
2024
This paper introduces a discrete memristor model and verifies the correctness of the model through circuit simulation. A six-dimensional discrete neural network was built by coupling the Rulkov neuron and the KTZ neuron. Dynamical analyses show that this neural network has multiple firing patterns when the memristor parameters and coupling coefficient are varied in the appropriate ranges, such as periodic firing, quasi-periodic firing, chaotic firing, and hyperchaotic firing. In addition, the coexisting multiple firing patterns and state transition phenomena of this neural network are revealed. Finally, the complexity analysis shows that the generated chaotic sequences have high pseudo-randomness, and the hardware implementation is completed in the Digital Signal Processor (DSP). This paper provides a reference for the study of memristive neural networks and communication encryption.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
