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 Computing and Applications
Erschienen in: Neural Computing and Applications 12/2018

11.11.2016 | Original Article

Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system

verfasst von: Muhammad Asif Zahoor Raja, Abbas Ali Shah, Ammara Mehmood, Naveed Ishtiaq Chaudhary, Muhammad Saeed Aslam

Erschienen in: Neural Computing and Applications | Ausgabe 12/2018

Einloggen

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

search-config
loading …

Abstract

In this study, strength of evolutionary computational intelligence based on genetic algorithms (GAs) is exploited for parameter identification of nonlinear Hammerstein controlled autoregressive (NHCAR) systems. The fitness function is constructed for the NHCAR system by defining an error function in the mean square sense. Unknown adjustable weights of the system are optimized with GAs, used as an effective tool for effective global search. Comparative analysis of the proposed scheme is made from true parameters of the systems for a number of scenarios based on different levels of signal-to-noise ratios. The validation of the performance is made through statistics based on sufficiently large number of runs using indices of mean absolute error, variance account for, and Thiel’s inequality coefficient as well as their global versions.
Vorheriger Artikel Pyramidal neural networks with evolved variable receptive fields
Nächster Artikel A solving method based on neural network for a class of multi-leader–follower games

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

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

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+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
Literatur
1.
Corbier C, El Badaoui M, Ugalde HMR (2015) Huberian approach for reduced order ARMA modeling of neurodegenerative disorder signal. Signal Process 113:273–284CrossRef
2.
Ding F, Wang Y, Ding J (2015) Recursive least squares parameter identification algorithms for systems with colored noise using the filtering technique and the auxilary model. Digit Signal Process 37:100–108CrossRef
3.
Raja MAZ, Chaudhary NI (2014) Adaptive strategies for parameter estimation of Box–Jenkins systems. IET Signal Process 8(9):968–980CrossRef
4.
Zhang T, Qian W, Zhang G, Ye F, Gao C, Zhao H (2016) Parameter estimation of MC-CDMA signals based on modified cyclic autocorrelation. Digit Signal Process 54:46–53CrossRef
5.
Chaudhary NI, Raja MAZ, Aslam MS, Ahmed N (2016) Novel generalization of Volterra LMS algorithm to fractional order with application to system identification. Neural Comput Appl. doi:10.​1007/​s00521-016-2548-5
6.
Raja MAZ, Chaudhary NI (2015) Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems. Signal Process 107:327–339CrossRef
7.
Wang Z, Ruimin H, Shao Z, Hou Z (2014) Parameter estimation in sparse representation based face hallucination. Digit Signal Process 31:28–34CrossRef
8.
Mao Y, Ding F (2015) Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique. Nonlinear Dyn 79(3):1745–1755CrossRefMATH
9.
Ding F, Liu XP, Liu G (2011) Identification methods for Hammerstein nonlinear systems. Digit Signal Process 21(2):215–238MathSciNetCrossRef
10.
Chen H, Ding F, Xiao Y (2015) Decomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation technique. Nonlinear Dyn 79(3):2027–2035CrossRefMATH
11.
12.
Mao Y, Ding F (2015) A novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systems. Digit Signal Process 46:215–225MathSciNetCrossRef
13.
Chaudhary NI, Raja MAZ (2015) Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms. Nonlinear Dyn 79(2):1385–1397MathSciNetCrossRefMATH
14.
Chaudhary NI, Raja MAZ (2015) Design of fractional adaptive strategy for input nonlinear Box–Jenkins systems. Signal Process 116:141–151CrossRef
15.
Shen Q, Ding F (2016) Hierarchical multi-innovation extended stochastic gradient algorithms for input nonlinear multivariable OEMA systems by the key-term separation principle. Nonlinear Dyn 85(1):499–507MathSciNetCrossRefMATH
16.
Ding F, Shi Y, Chen T (2007) Auxiliary model-based least-squares identification methods for Hammerstein output-error systems. Syst Control Lett 56(5):373–380MathSciNetCrossRefMATH
17.
Hu H, Ding R (2014) Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model. Nonlinear Dyn 76(1):777–784MathSciNetCrossRefMATH
18.
Li G et al (2011) Identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines. IEEE Trans Signal Process 59(5):2146–2159MathSciNetCrossRef
19.
Xiong W, Fan W, Ding R (2012) Least-squares parameter estimation algorithm for a class of input nonlinear systems. J Appl Math 2012, Article ID 684074. doi:10.​1155/​2012/​684074
20.
Chen H, Ding F (2015) Hierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systems. Circuits Syst Signal Process 34(1):61–75MathSciNetCrossRefMATH
21.
Chen H, Ding F (2013) Decomposition based recursive least squares parameter estimation for Hammerstein nonlinear controlled autoregressive systems. In: IEEE American control conference (ACC)
22.
Xiao Y, Song G, Liao Y, Ding R (2012) Multi-innovation stochastic gradient parameter estimation for input nonlinear controlled autoregressive models. Int J Control Autom Syst 10(3):639–643CrossRef
23.
Chaudhary NI, Raja MAZ, Khan JA, Aslam MS (2013) Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Sci World J 2013, Article ID 467276. doi:10.​1155/​2013/​467276
24.
Chaudhary NI, Raja MAZ, Khan AUR (2015) Design of modified fractional adaptive strategies for Hammerstein nonlinear control autoregressive systems. Nonlinear Dyn 82(4):1811–1830CrossRef
25.
26.
Chiroma H, Abdulkareem S, Herawan T (2015) Evolutionary neural network model for West Texas Intermediate crude oil price prediction. Appl Energy 142:266–273CrossRef
27.
28.
29.
30.
Raja MAZ, Khan JA, Chaudhary NI, Shivanian E (2016) Reliable numerical treatment of nonlinear singular Flierl–Petviashivili equations for unbounded domain using ANN, GAs, and SQP. Appl Soft Comput 38:617–636CrossRef
31.
Kahourzade S, Mahmoudi A, Mokhlis HB (2015) A comparative study of multi-objective optimal power flow based on particle swarm, evolutionary programming, and genetic algorithm. Electr Eng 97(1):1–12CrossRef
32.
Ugalde HMR, Carmona JC, Reyes-Reyes J, Alvarado VM, Mantilla J (2015) Computational cost improvement of neural network models in black box nonlinear system identification. Neurocomputing 166:96–108CrossRef
33.
Gotmare A, Patidar R, George NV (2015) Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model. Expert Syst Appl 42(5):2538–2546CrossRef
34.
Ugalde HMR, Carmona JC, Reyes-Reyes J, Alvarado VM, Corbier C (2015) Balanced simplicity–accuracy neural network model families for system identification. Neural Comput Appl 26(1):171–186CrossRef
35.
36.
Aledo JA, Gámez JA, Molina D (2016) Using metaheuristic algorithms for parameter estimation in generalized Mallows models. Appl Soft Comput 38:308–320CrossRef
37.
Da Ros S et al (2013) A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models. Appl Soft Comput 13(5):2205–2214CrossRef
38.
Mall S, Chakraverty S (2013) Comparison of artificial neural network architecture in solving ordinary differential equations. Adv Artif Neural Syst 2013:12MATH
39.
Jaddi NS, Abdullah S, Hamdan AR (2015) Optimization of neural network model using modified bat-inspired algorithm. Appl Soft Comput 37:71–86CrossRef
40.
41.
Raja MAZ, Shah FH, Ahad A, Khan NA (2016) Design of bio-inspired computational intelligence technique for solving steady thin film flow of Johnson–Segalman fluid on vertical cylinder for drainage problem. J Tiawan Inst Chem Eng. doi:10.​1016/​j.​jtice.​2015.​10.​020
42.
Gozde H (2015) Comparative analysis of swarm optimization-based control method for direct matrix converter. Electr Eng 97(3):181–193CrossRef
43.
44.
Raja MAZ, Khan JA, Behloul D, Haroon T, Siddiqui AM, Samar R (2015) Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation. Appl Soft Comput 26:244–256. doi:10.​1016/​j.​asoc.​2014.​10.​009 CrossRef
45.
Agrawal RK, Bawane NG (2015) Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery. Appl Soft Comput 28:217–225CrossRef
46.
Khan JA, Raja MAZ, Syam MI, Tanoli SAK, Awan SE (2015) Design and application of nature inspired computing approach for nonlinear stiff oscillatory problems. Neural Comput Appl 26(7):1763–1780CrossRef
47.
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan press, Ann arbor
48.
Zhu Z, Aslam MW, Nandi AK (2014) Genetic algorithm optimized distribution sampling test for M-QAM modulation classification. Signal Process 94:264–277CrossRef
49.
Alirezazadeh P, Fathi A, Abdali-Mohammadi F (2015) A genetic algorithm-based feature selection for kinship verification. IEEE Signal Process Lett 22(12):2459–2463CrossRef
50.
Yao L, Sethares WA (1994) Nonlinear parameter estimation via the genetic algorithm. IEEE Trans Signal Process 42(4):927–935CrossRef
51.
Pasolli E, Melgani F (2015) Genetic algorithm-based method for mitigating label noise issue in ECG signal classification. Biomed Signal Process Control 19:130–136CrossRef
52.
Arabali A et al (2013) Genetic-algorithm-based optimization approach for energy management. IEEE Trans Power Deliv 28(1):162–170CrossRef
53.
Stanković L, Popović-Bugarin V, Radenović F (2013) Genetic algorithm for rigid body reconstruction after micro-Doppler removal in the radar imaging analysis. Signal Process 93(7):1921–1932CrossRef
54.
Couceiro MS, Rocha RP, Fonseca Ferreira NM, Tenreiro Machado JA (2012) Introducing the fractional-order Darwinian PSO. Signal Image Video Process 6(3):343–350CrossRef
55.
Couceiro M, Ghamisi P (2016) Fractional order Darwinian particle swarm optimization. Springer International Publishing, Berlin, pp 11–20MATH
56.
Pires EJS, Tenreiro Machado JA, de Moura Oliveira PB, Boaventura Cunha J, Mendes L (2010) Particle swarm optimization with fractional-order velocity. Nonlinear Dyn 61(1-2):295–301CrossRefMATH
57.
Couceiro MS, Martins FML, Rocha RP, Ferreira NMF, Sivasundaram S (2012) Introducing the fractional order robotic Darwinian PSO. In: AIP conference proceedings-American Institute of Physics, vol 1493, no 1, p 242
58.
Ghamisi P, Couceiro MS, Martins FML, Benediktsson JA (2014) Multilevel image segmentation based on fractional-order Darwinian particle swarm optimization. IEEE Trans Geosci Remote Sens 52(5):2382–2394CrossRef
Metadaten
Titel
Bio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive system
verfasst von
Muhammad Asif Zahoor Raja
Abbas Ali Shah
Ammara Mehmood
Naveed Ishtiaq Chaudhary
Muhammad Saeed Aslam
Publikationsdatum
11.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2677-x

Weitere Artikel der Ausgabe 12/2018

Neural Computing and Applications 12/2018 Zur Ausgabe

Original Article

Probabilistic identification of subsurface gypsum geohazards using artificial neural networks

Original Article

A solving method based on neural network for a class of multi-leader–follower games

Original Article

Artificial neural network model for effective cancer classification using microarray gene expression data

Original Article

Novel bacterial foraging-based ANFIS for speed control of matrix converter-fed industrial BLDC motors operated under low speed and high torque

Original Article

Fast-forward solver for inhomogeneous media using machine learning methods: artificial neural network, support vector machine and fuzzy logic

Original Article

Pyramidal neural networks with evolved variable receptive fields