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• W2905888975 abstract "This brief is concerned with stability analysis for delayed neural networks (DNNs). By establishing polynomials and introducing slack variables reasonably, some improved delay-product type of auxiliary polynomial-based functions (APFs) is developed to exploit additional degrees of freedom and more information on extra states. Then, by constructing Lyapunov-Krasovskii functional using APFs and integrals of quadratic forms with high order scalar functions, a novel stability criterion is derived for DNNs, in which the benefits of the improved inequalities are fully integrated and the information on delay and its derivative is well reflected. By virtue of the advantages of APFs, more desirable performance is achieved through the proposed approach, which is demonstrated by the numerical examples." @default.
• W2905888975 created "2019-01-01" @default.
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• W2905888975 date "2019-08-01" @default.
• W2905888975 modified "2023-10-18" @default.
• W2905888975 title "Stability Analysis for Delayed Neural Networks via Improved Auxiliary Polynomial-Based Functions" @default.
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• W2905888975 doi "https://doi.org/10.1109/tnnls.2018.2877195" @default.
• W2905888975 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30575549" @default.
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