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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2019

13.06.2019

Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode

verfasst von: Lichao Feng, Jinde Cao, Jun Hu, Zhihui Wu, Leszek Rutkowski

Erschienen in: Neural Processing Letters | Ausgabe 3/2019

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Abstract

Recently, the random noises derived from discrete state observations are creatively designed to realize the role of stabilization for deterministic systems in the existing result. However, for a hybrid neural network, except for the factor of discrete state observations, one always needs to consider the factors of delays and discrete mode identifications. Hence, taking delays and discrete mode identifications into account for random noises is more reasonable and practical than the original work. Motivated by the idea above, this brief is to design delayed random noises derived from discrete state observations and discrete mode identifications to almost surely exponentially stabilize an unstable hybrid recurrent neural networks, by virtue of M-matrix and stochastic analysis methods.
Vorheriger Artikel Time Series Forecasting Using Neural Networks: Are Recurrent Connections Necessary?
Nächster Artikel Extended Synchronization Control for Switched Neural Networks with Multi Quantization Densities Based on a Persistent Dwell-Time Approach

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Metadaten
Titel
Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode
verfasst von
Lichao Feng
Jinde Cao
Jun Hu
Zhihui Wu
Leszek Rutkowski
Publikationsdatum
13.06.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10059-z

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