Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 6/2023

13.06.2023

Partial-Neurons-Based \(H_{\infty }\) State Estimation for Time-Varying Neural Networks Subject to Randomly Occurring Time Delays under Variance Constraint

verfasst von: Jun Hu, Yan Gao, Cai Chen, Junhua Du, Chaoqing Jia

Erschienen in: Neural Processing Letters | Ausgabe 6/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper discusses the issue of partial-neurons-based \(H_{\infty }\) state estimation for time-varying recurrent neural networks subject to randomly occurring time delays under variance constraint index. The measurement outputs are allowed to be available only at certain neurons. In addition, a random variable is introduced to model the randomly occurring time delays with certain occurrence probability. The aim is to propose the non-augmented partial-neurons-based state estimation strategy. Accordingly, some sufficient conditions are given to ensure two indices including the pre-determined \(H_{\infty }\) performance index and the error variance boundedness via the stochastic analysis approach. Finally, a simulation example is used to demonstrate the feasibility of presented partial-neurons-based \(H_{\infty }\) state estimation algorithm.
Vorheriger Artikel Hierarchical Neural Network with Serial Attention Mechanism for Review Sentiment Classifification
Nächster Artikel A New Hybrid Cuckoo Quantum-Behavior Particle Swarm Optimization Algorithm and its Application in Muskingum Model

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Peng T, Lu J, Tu Z, Lou J (2022) Finite-time stabilization of quaternion-valued neural networks with time delays: an implicit function method. Inf Sci 613:747–762
2.
Ding Y, Fu M, Luo P, Wu FX (2023) Network learning for biomarker discovery. Int J Netw Dyn Intell 2(1):51–65
3.
Wang M, Wang H, Zheng H (2022) A mini review of node centrality metrics in biological networks. Int J Netw Dyn Intell 1(1):99–110
4.
Tanaka G, Nakane R, Takeuchi T, Yamane T, Nakano D, Katayama Y, Hirose A (2020) Spatially arranged sparse recurrent neural networks for energy efficient associative memory. IEEE Trans Neural Netw Learn Syst 31(1):24–38MathSciNet
5.
Demin VA, Nekhaev DV, Surazhevsky IA, Nikiruy KE, Emelyanov AV, Nikolaev SN, Rylkov VV, Kovalchuk MV (2021) Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network. Neural Netw 134:64–75
6.
Li X, Li M, Yan P, Li G, Jiang Y, Luo H, Yin S (2023) Deep learning attention mechanism in medical image analysis: basics and beyonds. Int J Netw Dyn Intell 2(1):93–116
7.
Yao F, Ding Y, Hong S, Yang S-H (2022) A survey on evolved LoRa-based communication technologies for emerging internet of things applications. Int J Netw Dyn Intell 1(1):4–19
8.
Schuetz MJA, Brubaker JK, Katzgraber HG (2022) Combinatorial optimization with physics-inspired graph neural networks. Nat Mach Intell 4(4):367–377
9.
Bao G, Ma L, Yi X (2022) Recent advances on cooperative control of heterogeneous multi-agent systems subject to constraints: a survey. Syst Sci Control Eng 10(1):539–551
10.
Li J, Wang Z, Dong H, Ghinea G (2021) Outlier-resistant remote state estimation for recurrent neural networks with mixed time-delays. IEEE Trans Neural Netw Learn Syst 32(5):2266–2273MathSciNet
11.
Zou C, Li B, Du S, Chen X (2021) \(H_{\infty }\) state estimation for round-robin protocol-based Markovian jumping neural networks with mixed time delays. Neural Process Lett 53(6):4313–4330
12.
Li H, Wu P, Zeng N, Liu Y, Alsaadi FE (2022) A survey on parameter identification, state estimation and data analytics for lateral flow immunoassay: from systems science perspective. Int J Syst Sci 53(16):3556–3576MATH
13.
Zhao Y, He X, Ma L, Liu H (2022) Unbiasedness-constrained least squares state estimation for time-varying systems with missing measurements under round-robin protocol. Int J Syst Sci 53(9):1925–1941MathSciNetMATH
14.
Yu Y, Dong H, Wang Z, Li J (2019) Delay-distribution-dependent non-fragile state estimation for discrete-time neural networks under event-triggered mechanism. Neural Comput Appl 31(11):7245–7256
15.
Hou N, Dong H, Wang Z, Ren W, Alsaadi FE (2016) Non-fragile state estimation for discrete Markovian jumping neural networks. Neurocomputing 179:238–245
16.
Shen Y, Wang Z, Shen B, Alsaadi FE (2020) \(H_{\infty }\) state estimation for multi-rate artificial neural networks with integral measurements: a switched system approach. Inf Sci 539:434–446MATH
17.
Qu Y, Pang K (2020) State estimation for a class of artificial neural networks subject to mixed attacks: a set-membership method. Neurocomputing 411:239–246
18.
Meng X, Chen Y, Ma L, Liu H (2022) Protocol-based variance-constrained distributed secure filtering with measurement censoring. Int J Syst Sci 53(15):3322–3338MathSciNetMATH
19.
Wang L, Liu S, Zhang Y, Ding D, Yi X (2022) Non-fragile \(l_{2}\)-\(l_{\infty }\) state estimation for time-delayed artificial neural networks: an adaptive event-triggered approach. Int J Syst Sci 53(10):2247–2259MATH
20.
Wang X, Sun Y, Ding D (2022) Adaptive dynamic programming for networked control systems under communication constraints: a survey of trends and techniques. Int J Netw Dyn Intell 1(1):85–98
21.
Sakthivel R, Devi NB, Harshavarthini S, Kwon O (2022) Disturbance estimation and synchronization control design for nonlinear complex dynamical networks with input delays. Int J Robust Nonlinear Control 32(7):4281–4299MathSciNet
22.
Feng S, Yu H, Jia C, Gao P (2022) Joint state and fault estimation for nonlinear complex networks with mixed time-delays and uncertain inner coupling: Non-fragile recursive method. Syst Sci Control Eng 10(1):603–615
23.
Zhu Q (2019) Stabilization of stochastic nonlinear delay systems with exogenous disturbances and the event-triggered feedback control. IEEE Trans Autom Control 64(9):3764–3771MathSciNetMATH
24.
Zhu Q, Huang T (2021) \(H_{\infty }\) control of stochastic networked control systems with time-varying delays: the event-triggered sampling case. Int J Robust Nonlinear Control 31(18):9767–9781MathSciNet
25.
Tian Y, Yan H, Zhang H, Cheng J, Shen H (2022) Asynchronous output feedback control of hidden semi-Markov jump systems with random mode-dependent delays. IEEE Trans Autom Control 67(8):4107–4114MathSciNetMATH
26.
Suo J, Li N (2022) Observer-based synchronisation control for discrete-time delayed switched complex networks with coding-decoding approach. Int J Syst Sci 53(13):2711–2728MathSciNetMATH
27.
Yu L, Cui Y, Liu Y, Alotaibi ND, Alsaadi FE (2022) Sampled-based consensus of multi-agent systems with bounded distributed time-delays and dynamic quantisation effects. Int J Syst Sci 53(11):2390–2406MathSciNetMATH
28.
Suo J, Li N, Li Q (2021) Event-triggered \(H_{\infty }\) state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations. Neurocomputing 455:297–307
29.
Qian W, Xing W, Fei S (2021) \(H_{\infty }\) state estimation for neural networks with general activation function and mixed time-varying delays. IEEE Trans Neural Netw Learn Syst 32(9):3909–3918MathSciNet
30.
Zhao D, Wang Z, Wei G, Liu X (2021) Nonfragile \(H_{\infty }\) state estimation for recurrent neural networks with time-varying delays: On proportional-integral observer design. IEEE Trans Neural Netw Learn Syst 32(8):3553–3565MathSciNet
31.
Wang Y, Cao J, Wang H (2021) State estimation for Markovian coupled neural networks with multiple time delays via event-triggered mechanism. Neural Process Lett 53(2):893–906
32.
Cao Y, Maheswari K, Dharani S (2023) Improved summation inequality based state estimation for stochastic semi-Markovian jumping discrete-time neural networks with mixed delays and quantization. Neural Process Lett. 55(2):1919–1935
33.
Yu Y, Dong H, Wang Z, Ren W, Alsaadi FE (2016) Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties. Neurocomputing 182:18–24
34.
Song Y, Hu J, Chen D, Liu Y, Alsaadi FE, Sun G (2018) A resilience approach to state estimation for discrete neural networks subject to multiple missing measurements and mixed time-delays. Neurocomputing 272:74–83
35.
Liu F, Song Q, Wen G, Cao J, Yang X (2018) Bipartite synchronization in coupled delayed neural networks under pinning control. Neural Netw 108:146–154MATH
36.
Yang X, Song Q, Liang J, He B (2015) Finite-time synchronization of coupled discontinuous neural networks with mixed delays and nonidentical perturbations. J Frankl Inst-Eng Appl Math 352(10):4382–4406MathSciNetMATH
37.
Li J, Wang Z, Dong H, Fei W (2020) Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with uncertain transition probabilities. Neural Netw 130:143–151MATH
38.
Ding S, Wang Z, Wang J, Zhang H (2016) \(H_{\infty }\) state estimation for memristive neural networks with time-varying delays: the discrete-time case. Neural Netw 84:47–56MATH
39.
40.
Liu Y, Wang Z, Yuan Y, Liu W (2019) Event-triggered partial-nodes-based state estimation for delayed complex networks with bounded distributed delays. IEEE Trans Syst Man Cybern-Syst 49(6):1088–1098
41.
Hou N, Dong H, Wang Z, Liu H (2021) A partial-node-based approach to state estimation for complex networks with sensor saturations under random access protocol. IEEE Trans Neural Netw Learn Syst 32(11):5167–5178MathSciNet
42.
Hou N, Wang Z, Ho DWC, Dong H (2020) Robust partial-nodes-based state estimation for complex networks under deception attacks. IEEE Trans Cybern 50(6):2793–2802
43.
Liu Y, Wang Z, Ma L, Alsaadi FE (2019) A partial-nodes-based information fusion approach to state estimation for discrete-time delayed stochastic complex networks. Inf Fus 49:240–248
44.
Wang L, Zhao D, Wang Y-A, Ding D, Liu H (2022) Partial-neurons-based state estimation for artificial neural networks under constrained bit rate: the finite-time case. Neurocomputing 488:144–153
45.
Li J, Dong H, Wang Z, Bu X (2020) Partial-neurons-based passivity-guaranteed state estimation for neural networks with randomly occurring time delays. IEEE Trans Neural Netw Learn Syst 31(9):3747–3753MathSciNet
46.
Liu S, Wang Z, Shen B, Wei G (2021) Partial-neurons-based state estimation for delayed neural networks with state-dependent noises under redundant channels. Inf Sci 547:931–944MathSciNetMATH
47.
Shen B, Wang Z, Shu H, Wei G (2011) \(H_{\infty }\) filtering for uncertain time-varying systems with multiple randomly occurred nonlinearities and successive packet dropouts. Int J Robust Nonlinear Control 21(14):1693–1709MathSciNetMATH
Metadaten
Titel
Partial-Neurons-Based State Estimation for Time-Varying Neural Networks Subject to Randomly Occurring Time Delays under Variance Constraint
verfasst von
Jun Hu
Yan Gao
Cai Chen
Junhua Du
Chaoqing Jia
Publikationsdatum
13.06.2023
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 6/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-023-11312-2

Weitere Artikel der Ausgabe 6/2023

Neural Processing Letters 6/2023 Zur Ausgabe

OriginalPaper

An Innovative Information-Based Strategy for Epileptic EEG Classification

OriginalPaper

Hardware Spiking Neural Networks with Pair-Based STDP Using Stochastic Computing

OriginalPaper

Improving Regularization in Deep Neural Networks by Co-adaptation Trace Detection

OriginalPaper

Cascade Attentive Dropout for Weakly Supervised Object Detection

OriginalPaper

NV2P-RCNN: Feature Aggregation Based on Voxel Neighborhood for 3D Object Detection

OriginalPaper

PT-CNN: A Non-linear Lightweight Texture Image Classifier

Neuer Inhalt

    Bildnachweise