A single-layer asymmetric RNN with low hardware complexity for solving linear equations

Ansari, Mohd. Samar

doi:10.1016/j.neucom.2022.01.033

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Currently, relatively few studies have been conducted on ARNNs. Ansari ( 2022 ) suggested an ARNN with a single layer for solving linear equations. This research also provides a straightforward method for setting various connection weights.…”

Section: Introductionmentioning

confidence: 99%

Outer-synchronization criterions for asymmetric recurrent time-varying neural networks described by differential-algebraic system via data-sampling principles

Liu²,

Liu³

2022

Front. Comput. Neurosci.

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Asymmetric recurrent time-varying neural networks (ARTNNs) can enable realistic brain-like models to help scholars explore the mechanisms of the human brain and thus realize the applications of artificial intelligence, whose dynamical behaviors such as synchronization has attracted extensive research interest due to its superior applicability and flexibility. In this paper, we examined the outer-synchronization of ARTNNs, which are described by the differential-algebraic system (DAS). By designing appropriate centralized and decentralized data-sampling approaches which fully account for information gathering at the times tk and tki. Using the characteristics of integral inequalities and the theory of differential equations, several novel suitable outer-synchronization conditions were established. Those conditions facilitate the analysis and applications of dynamical behaviors of ARTNNs. The superiority of the theoretical results was then demonstrated by using a numerical example.

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Section: Introductionmentioning

confidence: 99%