Matches in SemOpenAlex for { <https://semopenalex.org/work/W2807391842> ?p ?o ?g. }
- W2807391842 endingPage "2835" @default.
- W2807391842 startingPage "2823" @default.
- W2807391842 abstract "This paper is concerned with energy-to-peak state estimation on static neural networks (SNNs) with interval time-varying delays. The objective is to design suitable delay-dependent state estimators such that the peak value of the estimation error state can be minimized for all disturbances with bounded energy. Note that the Lyapunov-Krasovskii functional (LKF) method plus proper integral inequalities provides a powerful tool in stability analysis and state estimation of delayed NNs. The main contribution of this paper lies in three points: 1) the relationship between two integral inequalities based on orthogonal and nonorthogonal polynomial sequences is disclosed. It is proven that the second-order Bessel-Legendre inequality (BLI), which is based on an orthogonal polynomial sequence, outperforms the second-order integral inequality recently established based on a nonorthogonal polynomial sequence; 2) the LKF method together with the second-order BLI is employed to derive some novel sufficient conditions such that the resulting estimation error system is globally asymptotically stable with desirable energy-to-peak performance, in which two types of time-varying delays are considered, allowing its derivative information is partly known or totally unknown; and 3) a linear-matrix-inequality-based approach is presented to design energy-to-peak state estimators for SNNs with two types of time-varying delays, whose efficiency is demonstrated via two widely studied numerical examples." @default.
- W2807391842 created "2018-06-13" @default.
- W2807391842 creator A5002870404 @default.
- W2807391842 creator A5074710425 @default.
- W2807391842 creator A5083874423 @default.
- W2807391842 creator A5090328003 @default.
- W2807391842 creator A5091433711 @default.
- W2807391842 date "2018-10-01" @default.
- W2807391842 modified "2023-10-18" @default.
- W2807391842 title "Energy-to-Peak State Estimation for Static Neural Networks With Interval Time-Varying Delays" @default.
- W2807391842 cites W1884323639 @default.
- W2807391842 cites W1891833740 @default.
- W2807391842 cites W1963839355 @default.
- W2807391842 cites W1968291684 @default.
- W2807391842 cites W2011164085 @default.
- W2807391842 cites W2011685813 @default.
- W2807391842 cites W2031138027 @default.
- W2807391842 cites W2031673504 @default.
- W2807391842 cites W2037686415 @default.
- W2807391842 cites W2044467507 @default.
- W2807391842 cites W2053832595 @default.
- W2807391842 cites W2076333832 @default.
- W2807391842 cites W2078804574 @default.
- W2807391842 cites W2094843491 @default.
- W2807391842 cites W2110460555 @default.
- W2807391842 cites W2118204568 @default.
- W2807391842 cites W2150765017 @default.
- W2807391842 cites W2153764327 @default.
- W2807391842 cites W2167706556 @default.
- W2807391842 cites W2197304904 @default.
- W2807391842 cites W2242049308 @default.
- W2807391842 cites W2250261304 @default.
- W2807391842 cites W2293327988 @default.
- W2807391842 cites W2329473580 @default.
- W2807391842 cites W2368784730 @default.
- W2807391842 cites W2410966350 @default.
- W2807391842 cites W2491068524 @default.
- W2807391842 cites W2515454492 @default.
- W2807391842 cites W2521394486 @default.
- W2807391842 cites W2530819434 @default.
- W2807391842 cites W2588591728 @default.
- W2807391842 cites W2592260377 @default.
- W2807391842 cites W2604681351 @default.
- W2807391842 cites W2605824083 @default.
- W2807391842 cites W2619229533 @default.
- W2807391842 cites W2626984388 @default.
- W2807391842 cites W2739104457 @default.
- W2807391842 cites W2752459249 @default.
- W2807391842 cites W2771237451 @default.
- W2807391842 cites W325253995 @default.
- W2807391842 doi "https://doi.org/10.1109/tcyb.2018.2836977" @default.
- W2807391842 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29994237" @default.
- W2807391842 hasPublicationYear "2018" @default.
- W2807391842 type Work @default.
- W2807391842 sameAs 2807391842 @default.
- W2807391842 citedByCount "35" @default.
- W2807391842 countsByYear W28073918422018 @default.
- W2807391842 countsByYear W28073918422019 @default.
- W2807391842 countsByYear W28073918422020 @default.
- W2807391842 countsByYear W28073918422021 @default.
- W2807391842 countsByYear W28073918422022 @default.
- W2807391842 countsByYear W28073918422023 @default.
- W2807391842 crossrefType "journal-article" @default.
- W2807391842 hasAuthorship W2807391842A5002870404 @default.
- W2807391842 hasAuthorship W2807391842A5074710425 @default.
- W2807391842 hasAuthorship W2807391842A5083874423 @default.
- W2807391842 hasAuthorship W2807391842A5090328003 @default.
- W2807391842 hasAuthorship W2807391842A5091433711 @default.
- W2807391842 hasConcept C105795698 @default.
- W2807391842 hasConcept C111458787 @default.
- W2807391842 hasConcept C11413529 @default.
- W2807391842 hasConcept C114614502 @default.
- W2807391842 hasConcept C134306372 @default.
- W2807391842 hasConcept C154945302 @default.
- W2807391842 hasConcept C185429906 @default.
- W2807391842 hasConcept C186370098 @default.
- W2807391842 hasConcept C2775924081 @default.
- W2807391842 hasConcept C2778067643 @default.
- W2807391842 hasConcept C2778112365 @default.
- W2807391842 hasConcept C28826006 @default.
- W2807391842 hasConcept C33923547 @default.
- W2807391842 hasConcept C34388435 @default.
- W2807391842 hasConcept C41008148 @default.
- W2807391842 hasConcept C47446073 @default.
- W2807391842 hasConcept C48103436 @default.
- W2807391842 hasConcept C54355233 @default.
- W2807391842 hasConcept C86803240 @default.
- W2807391842 hasConcept C90119067 @default.
- W2807391842 hasConceptScore W2807391842C105795698 @default.
- W2807391842 hasConceptScore W2807391842C111458787 @default.
- W2807391842 hasConceptScore W2807391842C11413529 @default.
- W2807391842 hasConceptScore W2807391842C114614502 @default.
- W2807391842 hasConceptScore W2807391842C134306372 @default.
- W2807391842 hasConceptScore W2807391842C154945302 @default.
- W2807391842 hasConceptScore W2807391842C185429906 @default.
- W2807391842 hasConceptScore W2807391842C186370098 @default.
- W2807391842 hasConceptScore W2807391842C2775924081 @default.
- W2807391842 hasConceptScore W2807391842C2778067643 @default.