Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Processing Letters
Published in: Neural Processing Letters 1/2021

13-01-2021

Mean Square Stabilization of Neural Networks with Weighted Try once Discard Protocol and State Observer

Authors: Linxiang Qi, Kaibo Shi, Chengdong Yang, Shiping Wen

Published in: Neural Processing Letters | Issue 1/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper investigates mean square stabilization problem of neural networks with weighted try once discard protocol and state observer. In general, sensor nodes access conflict happens frequently in the communication transmission networks. Aiming at this problem, we have adopted weighted try once discard protocol (WTOD) to reduce the occurrence of the conflict, The strategy is applied to discontinuous time systems, and only one node can obtain the access token to update signal transmission for the network. In addition, data transmission in the channel is large -scale and packet loss often occurs . Therefore, we adopt the method of quantized signal to reduce the pressure of channel transmission and ensure the stability of network transmission. As the system state is difficult to obtain, this paper design an observer to estimate the system state, the purpose of this paper is to guarantee the system to achieve the mean square stabilization with bounded output via the action of observer of signal output and controller, then solve the optimization problem of inequality through group of reasonable parameters of the controller and the observer. Finally, an example is given to prove the effectiveness of the proposed controller and observer.
previous article Pseudo Almost Automorphic Solutions of Neutral Type Clifford-Valued High-Order Hopfield Neural Networks with D Operator
next article Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Literature
1.
Lin S-S, Shih C-W (1999) Complete stability for standard cellular neural networks. Int J Bifurc Chaos 9(05):909–918MathSciNetCrossRef
2.
Guan Z-H, Chen G (1999) On delayed impulsive hopfield neural networks. Neural Netw 12(2):273–280CrossRef
3.
4.
Cao Y, Cao Y, Guo Z, Huang T, Wen S (2020) Global exponential synchronization of delayed memristive neural networks with reaction-diffusion terms. Neural Netw 123:70–81CrossRef
5.
Cohen MA, Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cyber 5:815–826MathSciNetCrossRef
6.
Xu W, Zhu S, Fang X, Wang W (2019) Adaptive anti-synchronization of memristor-based complex-valued neural networks with time delays. Physics A 535:1–10MathSciNet
7.
Xie X, Yue D, Peng C (2019) Observer design of discrete-time fuzzy systems based on an alterable weights method. IEEE Trans Cyber 99:1–11
8.
Xu Y, Liu C, Lu R, Su C-Y (2018) Remote estimator design for time-delay neural networks using communication state information. IEEE Trans Neural Netw Learn Syst 29:5149–5158MathSciNetCrossRef
9.
Guo Z, Liu L, Wang J (2019) Event based synchronization control for memristive neural networks with time-varying delay. IEEE Trans Neural Netw Learn Syst 30:2052–2066MathSciNetCrossRef
10.
Wang S, Guo Z, Wen S, Huang T, Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks, Neurocomputing
11.
Wen S, Liu W, Yang Y, Zeng Z, Huang T (2019) Generating realistic videos from keyframes with concatenated gans. IEEE Trans Circuits Syst Video Technol 29:2337–2348CrossRef
12.
Wen S, Liu W, Yang Y, Zhou P, Yan Z, Guo Z, Chen Y, Huang T (2020) Multi-label image classification via feature/label co-projection. IEEE Trans Syst Man Cyber: Syst 99:1–10
13.
Niu W, Feng Z, Zeng M, Feng B, Min Y, Cheng C, Zhou J (2019) Forecasting reservoir monthly runoff via ensemble empirical mode decomposition and extreme learning machine optimized by an improved gravitational search algorithm. Appl Soft Comput 82(105589):1–11
14.
Niu W, Feng Z, Cheng C, Zhou J (2018) Forecasting daily runoff by extreme learning machine based on quantum-behaved particle swarm optimization. J Hydrol Eng-ASCE 23(3):1–15CrossRef
15.
Niu W, Feng Z, Min Y, Feng B, Cheng C, Zhou J (2019) Comparison of multiple linear regression, artificial neural network, extreme learning machine and support vector machine in deriving hydropower reservoir operation rule. Water 11(1):88–100CrossRef
16.
Feng Z, Niu W, Cheng C (2019) China’s large-scale hydropower system: operation characteristics, modeling challenge and dimensionality reduction possibilities. Renew Energy 136:805–818CrossRef
17.
Feng Z, Niu W, Zhang R, Wang S, Zhou J, Cheng C (2019) Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization. J Hydrol 576:229–238CrossRef
18.
Wen S, Dong M, Yang Y, Zhou P, Huang T, Chen Y (2019) End-to-end detection-segmentation network for face labeling. IEEE Trans Emerg Topics Comput Intell 99:1–11
19.
Wen S, Chen J, Wu Y, Yan Z, Yang Y, Huang T (2020) Ckfo: Convolutional kernel first operated algorithm with applications in memristor-based convolutional neural networks. IEEE Trans Comput Aided Des Integr Circuits Syst 99:1–10CrossRef
20.
Tian Y, Yan H, Zhang H, Zhan X, Peng Y (2020) Dynamic output-feedback control of linear semi-markov jump systems with incomplete semi-markov kernel. Automatica 117:108997MathSciNetCrossRef
21.
Li Z, Yan H, Zhang H, Zhan X, Huang C (2019) Improved inequality-based functions approach for stability analysis of time delay system. Automatica 108:108416MathSciNetCrossRef
22.
Zhang H, Wang Z, Yan H, Yang F, Zhou X (2018) Adaptive event-triggered transmission scheme and \(h_{\infty }\) filtering co-design over a filtering network with switching topology. IEEE Trans Cyber 49(12):4296–4307CrossRef
23.
Ji X, Lu J, Lou J, Qiu J, Shi K, A unified criterion for global exponential stability of quaternion-valued neural networks with hybrid impulses, Int J Robust Nonlinear Control
24.
Ding D, Wang Z, Lam J, Shen B (2014) Finite-horizon control for discrete time-varying systems with randomly occurring nonlinearities and fading measurements. IEEE Trans Autom Control 60(9):2488–2493MathSciNetCrossRef
25.
Ge X, Yang F, Han Q-L (2017) Distributed networked control systems: A brief overview. Inf Sci 380:117–131CrossRef
26.
Zhang X-M, Han Q-L, Zhang B-L (2016) An overview and deep investigation on sampled-data-based event-triggered control and filtering for networked systems. IEEE Trans Ind Inf 13(1):4–16CrossRef
27.
Zhang X-M, Han Q-L, Yu X (2015) Survey on recent advances in networked control systems. IEEE Trans Ind Inf 12(5):1740–1752CrossRef
28.
Liu M, Ho DW, Niu Y (2010) Robust filtering design for stochastic system with mode-dependent output quantization. IEEE Trans Signal Process 58(12):6410–6416MathSciNetCrossRef
29.
Wang Y-W, Bian T, Xiao J-W, Wen C (2015) Global synchronization of complex dynamical networks through digital communication with limited data rate. IEEE Trans Neural Netw Learn Syst 26(10):2487–2499MathSciNetCrossRef
30.
Wang Y, Cao Y, Guo Z, Huang T, Wen S (2020) Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm. Appl Math Comput 383:125379MathSciNetMATH
31.
Wang S, Cao Y, Huang T, Chen Y, Wen S (2020) Event-triggered synchronization of multiple memristive neural networks with cyber-physical attacks. Inf Sci 518:361–375CrossRef
32.
Cao Y, Wang S, Wen S (2020) Exponential synchronization of switched neural networks with mixed time-varying delays via static/dynamic event-triggering rules. IEEE Access 8:338–347CrossRef
33.
34.
Zou L, Wang Z, Gao H, Liu X (2016) State estimation for discrete-time dynamical networks with time-varying delays and stochastic disturbances under the round-robin protocol. IEEE Trans Neural Netw Learn Syst 28(5):1139–1151CrossRef
35.
Walsh GC, Ye H, Bushnell LG (2002) Stability analysis of networked control systems. IEEE Trans Control Syst Technol 10(3):438–446CrossRef
36.
Tabbara M, Nesic D (2008) Input-output stability of networked control systems with stochastic protocols and channels. IEEE Trans Autom Control 53(5):1160–1175MathSciNetCrossRef
37.
Heemels WMH, Teel AR, Van de Wouw N, Nesic D (2010) Networked control systems with communication constraints: Tradeoffs between transmission intervals, delays and performance. IEEE Trans Autom Control 55(8):1781–1796MathSciNetCrossRef
38.
Zou L, Wang Z, Gao H (2016) Set-membership filtering for time-varying systems with mixed time-delays under round-robin and weighted try-once-discard protocols. Automatica 74:341–348MathSciNetCrossRef
39.
Liu K, Seuret A, Fridman E, Xia Y (2018) Improved stability conditions for discrete-time systems under dynamic network protocols. Int J Robust Nonlinear Control 28(15):4479–4499MathSciNetMATH
40.
Song Y, Wang Z, Ding D, Wei G, Robust \(h_2\)/\(h_\infty \) model predictive control for linear systems with polytopic uncertainties under weighted mef-tod protocol, IEEE Trans Syst Man, Cyber: Syst
41.
Liu K, Fridman E, Hetel L (2015) Networked control systems in the presence of scheduling protocols and communication delays. SIAM J Control Optim 53(4):1768–1788MathSciNetCrossRef
42.
Zou L, Wang Z, Han Q-L, Zhou D (2017) Ultimate boundedness control for networked systems with try-once-discard protocol and uniform quantization effects. IEEE Trans Autom Control 62(12):6582–6588MathSciNetCrossRef
Metadata
Title
Mean Square Stabilization of Neural Networks with Weighted Try once Discard Protocol and State Observer
Authors
Linxiang Qi
Kaibo Shi
Chengdong Yang
Shiping Wen
Publication date
13-01-2021
Publisher
Springer US
Published in
Neural Processing Letters / Issue 1/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10409-2

Other articles of this Issue 1/2021

Neural Processing Letters 1/2021 Go to the issue

OriginalPaper

A Novel Enhanced Naïve Bayes Posterior Probability (ENBPP) Using Machine Learning: Cyber Threat Analysis

OriginalPaper

A New Varying-Parameter Design Formula for Solving Time-Varying Problems

OriginalPaper

Network-Based Filtering for Descriptor Markovian Jump Systems with a Novel Neural Network Event-Triggered Scheme

OriginalPaper

Pseudo Almost Automorphic Solutions of Neutral Type Clifford-Valued High-Order Hopfield Neural Networks with D Operator

OriginalPaper

Computation of CNN’s Sensitivity to Input Perturbation

OriginalPaper

Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods