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Almost automorphic behaviours of nonlocal stochastic fuzzy Cohen–Grossberg lattice neural networks.

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Title: Almost automorphic behaviours of nonlocal stochastic fuzzy Cohen–Grossberg lattice neural networks.
Authors: Rao, Shaobin1 (AUTHOR), Zhang, Tianwei2 (AUTHOR) zhang@ynu.edu.cn
Source: International Journal of General Systems. Oct/Nov2024, Vol. 53 Issue 7/8, p1014-1041. 28p.
Subjects: Associative storage, Stochastic models, Integrals, Senses
Abstract: Discrete-time Cohen–Grossberg neural networks (CGNNs) play important role in feedback neural networks. The existing literatures of CGNNs only regarded integral order discrete-time models without other variables. This paper builds the lattice model for nonlocal stochastic fuzzy CGNNs with reaction diffusions by employing the finite difference and Mittag–Leffler time Euler difference techniques. Further, the existence of a unique bounded almost automorphic sequence solution in distribution and global exponential convergence in the mean-square sense to the achieved difference model are investigated. At last, by using the tool of Matlab and time Euler difference for the Brownian motions, an illustrative example with simulations is used to show the feasible of the works of the current paper. We think that this work can contribute to achieve some effective real tasks, e.g. content-addressable memory, auto-association, dynamic reconstruction of a chaotic process, etc. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of General Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Almost automorphic behaviours of nonlocal stochastic fuzzy Cohen–Grossberg lattice neural networks.
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  Data: <searchLink fieldCode="AR" term="%22Rao%2C+Shaobin%22">Rao, Shaobin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Tianwei%22">Zhang, Tianwei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhang@ynu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+General+Systems%22">International Journal of General Systems</searchLink>. Oct/Nov2024, Vol. 53 Issue 7/8, p1014-1041. 28p.
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  Label: Abstract
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  Data: Discrete-time Cohen–Grossberg neural networks (CGNNs) play important role in feedback neural networks. The existing literatures of CGNNs only regarded integral order discrete-time models without other variables. This paper builds the lattice model for nonlocal stochastic fuzzy CGNNs with reaction diffusions by employing the finite difference and Mittag–Leffler time Euler difference techniques. Further, the existence of a unique bounded almost automorphic sequence solution in distribution and global exponential convergence in the mean-square sense to the achieved difference model are investigated. At last, by using the tool of Matlab and time Euler difference for the Brownian motions, an illustrative example with simulations is used to show the feasible of the works of the current paper. We think that this work can contribute to achieve some effective real tasks, e.g. content-addressable memory, auto-association, dynamic reconstruction of a chaotic process, etc. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of General Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/03081079.2024.2340699
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      – Code: eng
        Text: English
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        PageCount: 28
        StartPage: 1014
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      – SubjectFull: Associative storage
        Type: general
      – SubjectFull: Stochastic models
        Type: general
      – SubjectFull: Integrals
        Type: general
      – SubjectFull: Senses
        Type: general
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      – TitleFull: Almost automorphic behaviours of nonlocal stochastic fuzzy Cohen–Grossberg lattice neural networks.
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              Text: Oct/Nov2024
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              Y: 2024
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              Value: 7/8
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