Multistability of Delayed Hybrid Impulsive Neural Networks With Application to Associative Memories

The important topic of multistability of continuous-and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistability of delayed hybrid NNs is studied in this paper with an emphasis on the impulse effects. Arising fro...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2019-05, Vol.30 (5), p.1537-1551
Main Authors: Hu, Bin, Guan, Zhi-Hong, Chen, Guanrong, Lewis, Frank L.
Format: Article
Language:English
Subjects:
Artificial neural networks
Associative memory
Basins
Biological neural networks
Biological system modeling
Coexistence
Delay effects
Economic models
Fault tolerance
impulse
multiple equilibria
multistability
neural network (NN)
Neural networks
Neurodynamics
Neurons
Storage capacity
time delay
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Summary:The important topic of multistability of continuous-and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistability of delayed hybrid NNs is studied in this paper with an emphasis on the impulse effects. Arising from the spiking phenomenon in biological networks, impulsive NNs provide an efficient model for synaptic interconnections among neurons. Using state-space decomposition, the coexistence of multiple equilibria of hybrid impulsive NNs is analyzed. Multistability criteria are then established regrading delayed hybrid impulsive neurodynamics, for which both the impulse effects on the convergence rate and the basins of attraction of the equilibria are discussed. Illustrative examples are given to verify the theoretical results and demonstrate an application to the design of associative memories. It is shown by an experimental example that delayed hybrid impulsive NNs have the advantages of high storage capacity and high fault tolerance when used for associative memories.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2018.2870553