Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay
The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization co...
Ausführliche Beschreibung
Autor*in: |
Sang, Hong [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022transfer abstract |
---|
Schlagwörter: |
---|
Umfang: |
12 |
---|
Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
---|---|
Übergeordnetes Werk: |
volume:505 ; year:2022 ; day:21 ; month:09 ; pages:154-165 ; extent:12 |
Links: |
---|
DOI / URN: |
10.1016/j.neucom.2022.07.021 |
---|
Katalog-ID: |
ELV058577491 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV058577491 | ||
003 | DE-627 | ||
005 | 20230626051149.0 | ||
007 | cr uuu---uuuuu | ||
008 | 221103s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.neucom.2022.07.021 |2 doi | |
028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica |
035 | |a (DE-627)ELV058577491 | ||
035 | |a (ELSEVIER)S0925-2312(22)00881-5 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 570 |q VZ |
084 | |a BIODIV |q DE-30 |2 fid | ||
084 | |a 35.70 |2 bkl | ||
084 | |a 42.12 |2 bkl | ||
100 | 1 | |a Sang, Hong |e verfasserin |4 aut | |
245 | 1 | 0 | |a Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
264 | 1 | |c 2022transfer abstract | |
300 | |a 12 | ||
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. | ||
520 | |a The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. | ||
650 | 7 | |a Switched generalized neural networks |2 Elsevier | |
650 | 7 | |a Event-triggered communication |2 Elsevier | |
650 | 7 | |a Synchronization control |2 Elsevier | |
650 | 7 | |a ℓ 2 - ℓ ∞ performance |2 Elsevier | |
650 | 7 | |a Asynchronous phenomenon |2 Elsevier | |
700 | 1 | |a Nie, Hong |4 oth | |
700 | 1 | |a Zhao, Jun |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
773 | 1 | 8 | |g volume:505 |g year:2022 |g day:21 |g month:09 |g pages:154-165 |g extent:12 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.neucom.2022.07.021 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a FID-BIODIV | ||
912 | |a SSG-OLC-PHA | ||
936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
936 | b | k | |a 42.12 |j Biophysik |q VZ |
951 | |a AR | ||
952 | |d 505 |j 2022 |b 21 |c 0921 |h 154-165 |g 12 |
author_variant |
h s hs |
---|---|
matchkey_str |
sanghongniehongzhaojun:2022----:vntigrdsnhoosycrnztocnrlosicegnrlzderle |
hierarchy_sort_str |
2022transfer abstract |
bklnumber |
35.70 42.12 |
publishDate |
2022 |
allfields |
10.1016/j.neucom.2022.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica (DE-627)ELV058577491 (ELSEVIER)S0925-2312(22)00881-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sang, Hong verfasserin aut Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay 2022transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Elsevier Nie, Hong oth Zhao, Jun oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 https://doi.org/10.1016/j.neucom.2022.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 505 2022 21 0921 154-165 12 |
spelling |
10.1016/j.neucom.2022.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica (DE-627)ELV058577491 (ELSEVIER)S0925-2312(22)00881-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sang, Hong verfasserin aut Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay 2022transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Elsevier Nie, Hong oth Zhao, Jun oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 https://doi.org/10.1016/j.neucom.2022.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 505 2022 21 0921 154-165 12 |
allfields_unstemmed |
10.1016/j.neucom.2022.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica (DE-627)ELV058577491 (ELSEVIER)S0925-2312(22)00881-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sang, Hong verfasserin aut Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay 2022transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Elsevier Nie, Hong oth Zhao, Jun oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 https://doi.org/10.1016/j.neucom.2022.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 505 2022 21 0921 154-165 12 |
allfieldsGer |
10.1016/j.neucom.2022.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica (DE-627)ELV058577491 (ELSEVIER)S0925-2312(22)00881-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sang, Hong verfasserin aut Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay 2022transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Elsevier Nie, Hong oth Zhao, Jun oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 https://doi.org/10.1016/j.neucom.2022.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 505 2022 21 0921 154-165 12 |
allfieldsSound |
10.1016/j.neucom.2022.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica (DE-627)ELV058577491 (ELSEVIER)S0925-2312(22)00881-5 DE-627 ger DE-627 rakwb eng 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sang, Hong verfasserin aut Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay 2022transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Elsevier Nie, Hong oth Zhao, Jun oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 https://doi.org/10.1016/j.neucom.2022.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 505 2022 21 0921 154-165 12 |
language |
English |
source |
Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 |
sourceStr |
Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:505 year:2022 day:21 month:09 pages:154-165 extent:12 |
format_phy_str_mv |
Article |
bklname |
Biochemie: Allgemeines Biophysik |
institution |
findex.gbv.de |
topic_facet |
Switched generalized neural networks Event-triggered communication Synchronization control ℓ 2 - ℓ ∞ performance Asynchronous phenomenon |
dewey-raw |
570 |
isfreeaccess_bool |
false |
container_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
authorswithroles_txt_mv |
Sang, Hong @@aut@@ Nie, Hong @@oth@@ Zhao, Jun @@oth@@ |
publishDateDaySort_date |
2022-01-21T00:00:00Z |
hierarchy_top_id |
ELV002603926 |
dewey-sort |
3570 |
id |
ELV058577491 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058577491</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626051149.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2022.07.021</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058577491</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(22)00881-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sang, Hong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Switched generalized neural networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Event-triggered communication</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Synchronization control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ℓ 2 - ℓ ∞ performance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Asynchronous phenomenon</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nie, Hong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Jun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:505</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:21</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:154-165</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2022.07.021</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">505</subfield><subfield code="j">2022</subfield><subfield code="b">21</subfield><subfield code="c">0921</subfield><subfield code="h">154-165</subfield><subfield code="g">12</subfield></datafield></record></collection>
|
author |
Sang, Hong |
spellingShingle |
Sang, Hong ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
authorStr |
Sang, Hong |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV002603926 |
format |
electronic Article |
dewey-ones |
570 - Life sciences; biology |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon Elsevier |
topic |
ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon |
topic_unstemmed |
ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon |
topic_browse |
ddc 570 fid BIODIV bkl 35.70 bkl 42.12 Elsevier Switched generalized neural networks Elsevier Event-triggered communication Elsevier Synchronization control Elsevier ℓ 2 - ℓ ∞ performance Elsevier Asynchronous phenomenon |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
h n hn j z jz |
hierarchy_parent_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
hierarchy_parent_id |
ELV002603926 |
dewey-tens |
570 - Life sciences; biology |
hierarchy_top_title |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV002603926 |
title |
Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
ctrlnum |
(DE-627)ELV058577491 (ELSEVIER)S0925-2312(22)00881-5 |
title_full |
Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
author_sort |
Sang, Hong |
journal |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
journalStr |
The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
500 - Science |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
zzz |
container_start_page |
154 |
author_browse |
Sang, Hong |
container_volume |
505 |
physical |
12 |
class |
570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Sang, Hong |
doi_str_mv |
10.1016/j.neucom.2022.07.021 |
dewey-full |
570 |
title_sort |
event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
title_auth |
Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
abstract |
The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. |
abstractGer |
The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. |
abstract_unstemmed |
The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
title_short |
Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay |
url |
https://doi.org/10.1016/j.neucom.2022.07.021 |
remote_bool |
true |
author2 |
Nie, Hong Zhao, Jun |
author2Str |
Nie, Hong Zhao, Jun |
ppnlink |
ELV002603926 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth |
doi_str |
10.1016/j.neucom.2022.07.021 |
up_date |
2024-07-06T19:26:04.898Z |
_version_ |
1803858964537933824 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV058577491</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626051149.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221103s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.neucom.2022.07.021</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001919.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV058577491</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0925-2312(22)00881-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sang, Hong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Event-triggered asynchronous synchronization control for switched generalized neural networks with time-varying delay</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The present research is concerned with the ℓ 2 - ℓ ∞ synchronization control for discrete-time switched generalized neural networks subject to time-varying delay. Distinct from the existing correlative results, a novel synchronization paradigm is established, in which the designed synchronization controller only contains the information of neuron states and switching signal at the certain triggering instants scheduled by the designed dynamic event-triggered mechanism. Also, an interesting asynchronous problem caused by the asynchronization of the switching and triggering actions is taken into consideration. For reducing the conservativeness of the conventional approaches, the time-dependent Lyapunov–Krasovskii functional for the resultant asynchronous situation is then constructed, and the correspondent sufficient conditions are formulated to ensure the exponential stability and weighted ℓ 2 - ℓ ∞ performance of the concerned synchronization error systems. Finally, the applicability and superiority of the developed synchronization technique are substantiated with two simulation examples.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Switched generalized neural networks</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Event-triggered communication</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Synchronization control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">ℓ 2 - ℓ ∞ performance</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Asynchronous phenomenon</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nie, Hong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhao, Jun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Liu, Yang ELSEVIER</subfield><subfield code="t">The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast</subfield><subfield code="d">2018</subfield><subfield code="d">an international journal</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV002603926</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:505</subfield><subfield code="g">year:2022</subfield><subfield code="g">day:21</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:154-165</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.neucom.2022.07.021</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">505</subfield><subfield code="j">2022</subfield><subfield code="b">21</subfield><subfield code="c">0921</subfield><subfield code="h">154-165</subfield><subfield code="g">12</subfield></datafield></record></collection>
|
score |
7.400879 |