Domain-Structured Chaos in a Hopfield Neural Network

Date

2019-12-30

Author

Akhmet, Marat

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In this paper, we provide a new method for constructing chaotic Hopfield neural networks. Our approach is based on structuring the domain to form a special set through the discrete evolution of the network state variables. In the chaotic regime, the formed set is invariant under the system governing the dynamics of the neural network. The approach can be viewed as an extension of the unimodality technique for one-dimensional map, thereby generating chaos from higher-dimensional systems. We show that the discrete Hopfield neural network considered is chaotic in the sense of Devaney, Li-Yorke, and Poincare. Mathematical analysis and numerical simulation are provided to confirm the presence of chaos in the network.

Subject Keywords

Modelling and Simulation, Applied Mathematics

URI

https://hdl.handle.net/11511/57687

Journal

INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS

DOI

https://doi.org/10.1142/s0218127419502055

Collections

Department of Mathematics, Article

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Citation Formats

M. Akhmet, “Domain-Structured Chaos in a Hopfield Neural Network,” INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57687.