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
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2017

10.03.2017

Normalised Spline Adaptive Filtering Algorithm for Nonlinear System Identification

verfasst von: Sihai Guan, Zhi Li

Erschienen in: Neural Processing Letters | Ausgabe 2/2017

Einloggen

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

search-config
loading …

Abstract

This paper proposed a normalised spline adaptive filtering algorithm to improve the stability of spline adaptive filtering (SAF) algorithm against the eigenvalue spread of the autocorrelation matrix of the input signal. The new adaptive filtering algorithm is based on the normalised least mean square (NLMS) approach and the value range of the learning rate in this algorithm is specified. This algorithm is called SAF-NLMS. In this work, first the derivation of the SAF-NLMS algorithm is given. Second, detailed convergence and the computational complexity analyses are carried out. Finally, the performance of the proposed algorithm is tested according to artificial datasets and real datasets. The achieved results present actually good performance. So, in practical engineering, the algorithm can be used to solve the problem of modeling or identification of nonlinear systems.
Vorheriger Artikel Cascaded Convolutional Neural Networks for Aspect-Based Opinion Summary
Nächster Artikel Efficient Color Texture Classification Using Color Monogenic Wavelet Transform

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.
Nerrand O, Roussel-Ragot P, Personnaz L, Dreyfus G (1993) Neural networks and nonlinear adaptive filtering: unifying concepts and new algorithms. Neural Comput 5(5):165–199CrossRef
2.
Barreto GA, Souza LG (2006) Adaptive filtering with the self-organizing map: a performance comparison. Neural Netw 19(6–7):785–798CrossRefMATH
3.
Barreto GA, Barros ALBP (2014) On the design of robust linear pattern classifiers based on M-estimators. Neural Process Lett 42(1):1–19
4.
Kong XY, Hu CH, Han CZ (2009) A self-stabilizing neural algorithm for total least squares filtering. Neural Process Lett 30(3):257–271CrossRef
5.
Widrow B, Lehr MA (1990) 30 years of adaptive neural network, perceptron, madaline, and back propagation. Proc IEEE 78(9):1415–1442CrossRef
6.
Kim JH, Zhang W, Ryu SK, Oh YS (2012) An ADALINE neural network with truncated momentum for system identification of linear time varying systems. IEEE Int Conf Ind Technol 2012:292–297
7.
Aouiti C, M’Hamdi MS, Touati A (2016) Pseudo almost automorphic solutions of recurrent neural networks with time-varying coefficients and mixed delays. Neural Process Lett 45(1):121–140CrossRef
8.
Egrioglu E, Yolcu U, Aladag CH, Bas E (2015) Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Neural Process Lett 41(2):249–258CrossRef
9.
Ljung L (1999) System identification: theory for the user, 2nd edn. Prentice Hall, Englewood CliffsMATH
10.
Prawin J, Rao ARM (2017) Nonlinear identification of MDOF systems using Volterra series approximation. Mech Syst Signal Process 84:58–77CrossRef
11.
LeCaillec J-M (2011) Spectral inversion of second order Volterra models based on the blind identification of Wiener models. Signal Process 91(11):2541–2555CrossRefMATH
12.
Sicuranza GL, Carini A (2011) A generalized FLANN filter for nonlinear active noise control. IEEE Trans Audio Speech Lang Process 19(8):2412–2417CrossRef
13.
Richard C, Bermudez J, Honeine P (2009) Online prediction of time series data with kernels. IEEE Trans Signal Process 57(3):1058–1067MathSciNetCrossRef
14.
Engel Y, Mannor S, Meir R (2012) Kernel recursive least squares. IEEE Trans Signal Process 52(5):2275–2285MathSciNetMATH
15.
Liu W, Príncipe JC, Haykin S (2009) Extended kernel recursive least squares algorithm. IEEE Trans Signal Process 57(10):3801–3814MathSciNetCrossRef
16.
Liu W, Príncipe JC, Simon H (2008) Kernel affine projection algorithms. EURASIP J Adv Signal Process 2008(1):1–12CrossRefMATH
17.
Chen B, Zhao S, Zhu P, Principe JC (2012) Quantized kernel least mean square algorithm. IEEE Trans Neural Netw Learn Syst 23(1):22–32CrossRef
18.
Parreira WD, Bermudez JCM, Richard C, Tourneret J-Y (2012) Stochastic behavior analysis of the Gaussian kernel least-mean-square algorithm. IEEE Trans Signal Process 60(5):2208–2222MathSciNetCrossRef
19.
Scarpiniti M, Comminiello D, Parisi R, Uncini A (2013) Nonlinear spline adaptive filtering. Signal Process 93(4):772–783CrossRef
20.
Scarpiniti M, Comminiello D, Parisi R, Uncini A (2015) Nonlinear system identification using IIR spline adaptive filters. Signal Process 108(108):30–35CrossRef
21.
Scarpiniti M, Comminiello D, Parisi R, Uncini A (2014) Hammerstein uniform cubic spline adaptive filters: learning and convergence properties. Signal Process 100(7):112–123CrossRef
22.
Patel V, George NV (2016) Compensating acoustic feedback in feed-forward active noise control systems using spline adaptive filters. Signal Process 120:448–455CrossRef
23.
Scarpiniti M, Comminiello D, Scarano G, Parisi R (2016) Steady-state performance of spline adaptive filters. IEEE Trans Signal Process 64(4):816–828MathSciNetCrossRef
24.
Scarpiniti M, Comminiello D, Parisi R, Uncini A (2015) Novel cascade spline architectures for the identification of nonlinear systems. IEEE Trans Circuits Syst I 62(7):1825–1835MathSciNetCrossRef
25.
Yin L, Astola J, Neuvo Y (1993) A new class of nonlinear filters-neural filters. IEEE Trans Signal Process 41(3):1201–1222CrossRefMATH
26.
Widrow B (2005) Thinking about thinking: the discovery of the LMS algorithm. IEEE Signal Process Mag 22(1):100–106CrossRef
27.
Sayin MO, Vanli ND, Kozat SS (2013) A novel family of adaptive filtering algorithms based on the logarithmic cost. IEEE Trans Signal Process 62(17):4411–4424MathSciNetCrossRef
28.
Bershad NJ (1986) Analysis of the normalized LMS algorithm with Gaussian inputs. IEEE Trans Acoust Speech Signal Process 34(4):793–806CrossRef
29.
Metadaten
Titel
Normalised Spline Adaptive Filtering Algorithm for Nonlinear System Identification
verfasst von
Sihai Guan
Zhi Li
Publikationsdatum
10.03.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9606-6

Weitere Artikel der Ausgabe 2/2017

Neural Processing Letters 2/2017 Zur Ausgabe

OriginalPaper

Delay-Dependent Passivity and Stability Analysis for a Class of Memristor-Based Neural Networks with Time Delay in the Leakage Term

OriginalPaper

Efficient Color Texture Classification Using Color Monogenic Wavelet Transform

OriginalPaper

The Sampled-data Exponential Stability of BAM with Distributed Leakage Delays

OriginalPaper

New Criteria on Exponential Lag Synchronization of Switched Neural Networks with Time-Varying Delays

OriginalPaper

Multivariate Chaotic Time Series Prediction Based on ELM–PLSR and Hybrid Variable Selection Algorithm

OriginalPaper

Scene Classification Using Multi-Resolution WAHOLB Features and Neural Network Classifier

Neuer Inhalt

    Bildnachweise