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Optical Memory and Neural Networks

Erschienen in:

01.10.2019

Global Mittag-Leffler Stability of Fractional Hopfield Neural Networks with δ-Inverse Hölder Neuron Activations

verfasst von: Xiaohong Wang, Huaiqin Wu

Erschienen in: Optical Memory and Neural Networks | Ausgabe 4/2019

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Abstract

In this paper, the global Mittag-Leffler stability of fractional Hopfield neural networks (FHNNs) with \(\delta \)-inverse hölder neuron activation functions are considered. By applying the Brouwer topological degree theory and inequality analysis techniques, the proof of the existence and uniqueness of equilibrium point is addressed. By constructing the Lure’s Postnikov-type Lyapunov functions, the global Mittag-Leffler stability conditions are achieved in terms of linear matrix inequalities (LMIs). Finally, three numerical examples are given to verify the validity of the theoretical results.

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Titel: Global Mittag-Leffler Stability of Fractional Hopfield Neural Networks with δ-Inverse Hölder Neuron Activations
verfasst von: Xiaohong Wang
Huaiqin Wu
Publikationsdatum: 01.10.2019
Verlag: Pleiades Publishing
Erschienen in: Optical Memory and Neural Networks / Ausgabe 4/2019
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI: https://doi.org/10.3103/S1060992X19040064

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