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 7/2015

01.10.2015 | Original Article

Design and application of nature inspired computing approach for nonlinear stiff oscillatory problems

verfasst von: Junaid Ali Khan, Muhammad Asif Zahoor Raja, Muhammed I. Syam, Shujaat Ali Khan Tanoli, Saeed Ehsan Awan

Erschienen in: Neural Computing and Applications | Ausgabe 7/2015

Einloggen

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

search-config
loading …

Abstract

In this paper, meta-heuristic intelligent approaches are developed for handling nonlinear oscillatory problems with stiff and non-stiff conditions. The mathematical modeling of these oscillators is accomplished using feed-forward artificial neural networks (ANNs) in the form of an unsupervised manner. The accuracy as well as efficiency of the model is subject to the tuning of adaptive parameters for ANNs that are highly stochastic in nature. These optimal weights are carried out with swarm intelligence and pattern search methods hybridized with an efficient local search technique based on constraints minimization known as active set algorithm. The proposed schemes are validated on various stiff and non-stiff variants of the oscillator. The significance, applicability and reliability of the proposed scheme are well established based on comparison made with the results of standard numerical solver.
Vorheriger Artikel Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System
Nächster Artikel Exponential stability of inertial BAM neural networks with time-varying delay via periodically intermittent control

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.
Mickens RE, Oyedeji K (2010) Comments on the general dynamics of the nonlinear oscillator. Mech Syst Signal Process 24:2076–2095CrossRef
2.
Kovacic I (2011) Forced vibrations of oscillators with a purely nonlinear power-form restoring force. J Sound Vib 330:4313–4327CrossRef
3.
Balaram B, Narayanan MD, RajendrakumarAli PK (2012) Optimal design of multi-parametric nonlinear systems using a parametric continuation based Genetic Algorithm approach. Nonlinear Dyn 67:2759–2777CrossRef
4.
Feng J, Zhu W-Q, Liu ZH (2011) Stochastic optimal time-delay control of quasi-integrable Hamiltonian systems. Commun Nonlinear Sci Numer Simulat 16:2978–2984MathSciNetCrossRefMATH
5.
Erneux T, Baer SM, Mandel P (1987) Subharmonic bifurcation and bistability of periodic solutions in a periodically modulated laser. Phys Rev A 35:1165–1171CrossRef
6.
Kaya M, Higuchi H (2010) Nonlinear elasticity and an 8-Nm working stroke of a single myosin molecules in myofilaments. Science 329(5992):686–689
7.
8.
Hamdan MN, shabaneh NH (1997) On the large amplitude free vibrations of a restained uniform beam carrying an intermediate lumped mass. J Sound Vib 199:711–736CrossRef
9.
10.
11.
12.
Belendz A, Hernandez A, Belendz T, Neipp C, Marques A (2006) Asymptotic representation of the period of nonlinear oscillator. J Sound Vib 299:406–408
13.
Kalmar T, Erneux T (2008) Approximating a small and large amplitude periodic orbits of the oscillator. J Sound Vib 313:806–811CrossRef
14.
Kerschen G, Worden K, Vakakis AF, Golinval JC (2006) Past, present and future of nonlinear system identification in structural dynamics. Mech Syst Signal Process 20:505–592CrossRef
15.
Raja MAZ, Sabir Z, Mahmood N, Alaidarous ES, Khan JA (2014) Stochastic solvers based on variants of genetic algorithms for solving nonlinear equations. Neural Comput Appl. doi:10.​1007/​s00521-014-1676-z
16.
Anastassi AA (2014) Constructing Runge–Kutta methods with the use of artificial neural networks. Neural Comput Appl 25(1):229–236CrossRef
17.
Mokeddem D, Khellaf A (2012) Optimal feeding profile for a fuzzy logic controller in a bioreactors using genetic algorithm. Nonlinear Dyn 67:2835–2845MathSciNetCrossRef
18.
Chakraverty S, Mall S (2014) Regression-based weight generation algorithm in neural network for solution of initial and boundary value problems. Neural Comput Appl 25(3–4):585–594CrossRef
19.
Raja MAZ, Qureshi IM, Khan JA (2011) Swarm intelligent optimized neural networks for solving fractional differential equations. Int J Innov Comput Inf Control 7(11):6301–6318
20.
Raja MAZ, Khan JA, Qureshi IM (2010) Heuristic computational approach using swarm intelligence in solving fractional differential equations. GECCO (Companion) 2010:2023–2026
21.
Raja MAZ, Khan JA, Qureshi IM (2011) Swarm intelligence optimized neural network for solving fractional order systems of Bagley-Tervik equation. Eng Intell Syst 1:41–51
22.
Zahoor RMA, Khan JA, Qureshi IM (2009) Evolutionary computation technique for solution of Riccati differential equation of arbitrary order. World Acad Sci Eng Technol 58:303–309
23.
Khan JA, Raja MAZ, Qureshi IM (2011) Novel approach for van der Pol oscillator on the continuous time domain. Chin Phys Lett 28(11):110205CrossRef
24.
Khan JA, Raja MAZ, Qureshi IM (2011) Hybrid evolutionary computational approach: application to van der Pol oscillator. Int J Phys Sci 6(31):7247–7261
25.
Raja MAZ, Khan JA, Ahmad SI, Qureshi IM Numeriacal treatment of Painleve equation I using neural networks and stochastic solvers. Innovation in Machine-3, Studies in Computational intelligence, book series Springer, vol 442, Sep 2012
26.
Raja MAZ, Khan JA, Ahmad SI, Qureshi IM (2012) Solution of the Painlevé equation-I using neural network optimized with swarm intelligence. Comput Intell Neurosci 2012:1–10. ID 721867
27.
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
28.
Raja MAZ, Khan JA, Shah SM, Bhahoal D, Samar R (2014) Comparison of three unsupervised neural network models for first Painleve´ Transcendent. Neural Comput Appl. Accepted 30-11-2014. doi:10.​1007/​s00521-014-1774-y
29.
Khan JA, Raja MAZ, Qureshi IM (2011) Numerical treatment of nonlinear Emden-Fowler equation using stochastic technique. Ann Math Artif Intell 63(2):185–207MathSciNetCrossRef
30.
Raja MAZ (2014) Unsupervised neural networks for solving Troesch’s problem. Chin Phys B 23(1):018903CrossRef
31.
32.
33.
Raja MAZ, Samar R (2014) Numerical treatment of nonlinear MHD Jeffery-Hamel problems using stochastic algorithms. Comput Fluids 91:28–46MathSciNetCrossRef
34.
35.
36.
37.
Raja MAZ, Ahmad SI, Samar R (2013) Neural network optimized with evolutionary computing technique for solving the 2-dimensional Bratu problem. Neural Comput Appl 23(7–8):2199–2210. doi:10.​1007/​s00521-012-1170-4 CrossRef
38.
Raja MAZ, Samar R, Rashidi MM (2014) Application of three unsupervised neural network models to singular nonlinear BVP of transformed 2D Bratu equation. Neural Comput Appl. Published online 17-06-2014. doi:10.​1007/​s00521-014-1641-x
39.
Raja MAZ, Ahmad SI, Samar R (2014) Solution of the 2-dimensional Bratu problem using neural network, swarm intelligence and sequential quadratic programming. Neural Comput Appl. Published online 06-07-2014. doi:10.​1007/​s00521-014-1664-3
40.
41.
42.
Rarisi Daniel R et al (2003) solving differential equations with unsupervised neural networks. Chem Eng Process 42:715–721CrossRef
43.
Aarts LP, Van Der Veer P (2001) Neural network method for solving the partial differential equations. Neural Process Lett 14:261–271CrossRefMATH
44.
Hornik K, Stichcombr M, White H (1990) Universal approximation of an unknown mapping and its derivatives using multilayer feed forward networks. Neural Netw 3:551–560CrossRef
45.
Meada AJ, Fernandez AA (1994) The numerical solution of linear ordinary differential equation by feedforward neural network. Math Comput Model 19:1–25CrossRef
46.
Raja MAZ, Khan JA, Qureshi IM (2011) Solution of fractional order system of Bagley-Torvik equation using evolutionary computational intelligence. J Math Probl Eng 2011:1–18
47.
Zhao HM, Chen KZ (2002) Neural network for solving systems of nonlinear equations. Acta Electronica 30:601–604
48.
Khan JA, Raja MAZ, Qureshi IM (2011) Stochastic computational approach for complex non-linear ordinary differential equations. Chin Phys Lett 28(2):020206MathSciNetCrossRef
49.
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, IEEE Service Center, vol. 4. Piscataway, NJ, pp 1942–1948
50.
Lee KC, Jhang JY (2006) Application of particle swarm optimization algorithm to the optimization of unequally spaced antenna arrays. J Electromagn Wave Appl 20:2001–2012
51.
Coello CA, Pulido GT, Lechuga MS (2004) handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comput 8:256–279CrossRef
52.
Pan I, Korre A, Das S, Durucan S (2012) Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise. Nonlinear Dyn 70:2445–2461MathSciNetCrossRef
53.
Gao Z, Liao X (2012) Rational approximation for fractional-order system by particle swarm optimization. Nonlinear Dyn 67:1387–1395MathSciNetCrossRefMATH
54.
Raja MAZ, Khan JA, Qureshi IM (2010) A new stochastic approach for solution of Riccati differential equation of fractional order. Ann Math Artif Intell 60(3–4):229–250MathSciNetCrossRefMATH
55.
Sun J, Liu X (2013) A novel APSO-aided maximum likelihood identification method for Hammerstein systems. Nonlinear Dyn 73:449–462CrossRefMATH
56.
Lee KY, El-Sharkawi MA (2008) Modern heuristic optimization techniques: theory and application to power systems. Wiley, HobokenCrossRef
57.
Kennedy J, Eberhart R (2001) Swarm intelligence, 1st edn. Academic press, San Diego
58.
Hsu CH, Tsou CS, Yu FJ (2009) Multicriteria tradeoffs in inventory control using memetic particle swarm optimization. Int J Innov Comput Inf Control 5:3755–3768
59.
Hooke R, Jeeves R (1961) Direct search solution of numerical and statistical problems. J Assoc Comput Mach 8:212–229CrossRefMATH
60.
Pelillo M, Torsello A (2006) Payoff-monotonic game dynamics and the maximum clique problem. Neural Comput 18(5):1215–1258
61.
Huang Z et al (2003) Applying SVM in license plate character recognition. J Comput Eng 29(5):192–194
62.
Ghosh A, Chowdhury A, Giri R, Das S, Abraham A (2010) A hybrid evolutionary direct search technique for solving Optimal Control problems. HIS 125-130
63.
Fan LT, Hwang YH, Hwang CL (1970) Applications of modern optimal control theory to environmental control of confined spaces and life support systems. Part 3- optimal control of systems in which straight variables have equally constraints at the final process time, vol 5, no 3–4, pp 125–136
64.
Wang L (2005) Support vector machines: theory and application. Springer, Berlin
65.
Metadaten
Titel
Design and application of nature inspired computing approach for nonlinear stiff oscillatory problems
verfasst von
Junaid Ali Khan
Muhammad Asif Zahoor Raja
Muhammed I. Syam
Shujaat Ali Khan Tanoli
Saeed Ehsan Awan
Publikationsdatum
01.10.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1841-z

Weitere Artikel der Ausgabe 7/2015

Neural Computing and Applications 7/2015 Zur Ausgabe

Original Article

Low-resolution degradation face recognition over long distance based on CCA

Original Article

A new security approach for public transport application against tag cloning with neural network-based pattern recognition

Original Article

Importance of hybrid models for forecasting of hydrological variable

Original Article

Crossover-based artificial bee colony algorithm for constrained optimization problems

Original Article

Prediction and characterization of surface roughness using sandblasting and acid etching process on new non-toxic titanium biomaterial: adaptive-network-based fuzzy inference System

Original Article

Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge

Premium Partner

    Bildnachweise