Global Stability of Phase-Change Neural Networks With Mixed Time Delays.

abstract

• Phase-change memory (PCM) is a novel type of nonvolatile memory and is suitable for artificial neural synapses. This article investigates the Lagrange global exponential stability (LGES) of a class of PCNNs with mixed time delays. First, based on the conductivity characteristics of PCM, a piecewise equation is established to describe the electrical conductivity of PCM. By using the proposed piecewise equation to simulate the neural synapses, a novel PCNN with discrete and distributed time delays is proposed. Then, using comparative theory and fundamental inequalities, the LGES conditions based on the M -matrix are proposed in the sense of Filippov, and the exponential attractive set (EAS) is obtained based on M -matrix and external input. Moreover, the Lyapunov global exponential stability (GES) conditions of PCNNs without external input are obtained by using the inequality technique and eigenvalue theory, which is a form of M -matrix. Finally, two simulation examples are given to verify the validity of the obtained results.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Dong, T., Song, Y., Li, H., Wang, X., & Huang, T.

complete list of authors

• Dong, Tao||Song, Yadi||Li, Huaqing||Wang, Xin||Huang, Tingwen

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Neural Networks and Learning Systems  Journal

keywords

• 46 Information And Computing Sciences
• 4611 Machine Learning

PubMed Central ID

• 39208048

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2024.3445116

start page

• 1

end page

• 11

volume

• PP

issue

• 99