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 Computing and Applications
Published in: Neural Computing and Applications 10/2019

05-03-2018 | Original Article

Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems

Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Rabia Bibi, Naveed Ishtiaq Chaudhary, Muhammad Saeed Aslam

Published in: Neural Computing and Applications | Issue 10/2019

Log in

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

search-config
loading …

Abstract

Aim of this research is to explore the strength of evolutionary and swarm intelligence techniques for parameter identification of control autoregressive moving average (CARMA) systems. The fitness function for CARMA system identification problem is formulated through error function created in mean square sense, and learning of unknown parameters of the system model is carried out with an effective global search techniques based on genetic algorithms and particle swarm optimization algorithm. Comparative study of the design methodology is conducted from actual parameters of the systems for different values of noise variance and degree of freedom in CARMA identification model. The correctness of the proposed scheme is validated through the results of various performance measures based on mean absolute error, mean weight deviation, variance account for and Theil’s inequality coefficient, and their global variants for sufficiently large number of independent runs.
previous article Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques
next article GEP and MLR approaches for the prediction of reference evapotranspiration

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

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+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
Literature
1.
Li H, Xu L, Zhang Z (2017) Parameter estimation of maneuvering target using maximum likelihood estimation for MIMO radar with colocated antennas. J Comput Commun 5(03):69CrossRef
2.
Ding F (2014) State filtering and parameter estimation for state space systems with scarce measurements. Signal Process 104:369–380CrossRef
3.
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
4.
Wang Y, Ding F (2016) Recursive parameter estimation algorithms and convergence for a class of nonlinear systems with colored noise. Circuits Syst Signal Process 35(10):3461–3481MathSciNetMATHCrossRef
5.
Shen Q, Ding F (2016) Least squares identification for Hammerstein multi-input multi-output systems based on the key-term separation technique. Circuits Syst Signal Process 35(10):3745–3758MathSciNetMATHCrossRef
6.
Ding F, Liu PX, Liu G (2010) Gradient based and least-squares based iterative identification methods for OE and OEMA systems. Digit Signal Process 20(3):664–677CrossRef
7.
Wang C, Tang T (2014) Several gradient-based iterative estimation algorithms for a class of nonlinear systems using the filtering technique. Nonlinear Dyn 77(3):769–780MathSciNetMATHCrossRef
8.
Wang X, Ding F, Alsaadi FE, Hayat T (2016) Convergence analysis of the hierarchical least squares algorithm for bilinear-in-parameter systems. Circuits Syst Signal Process 35(12):4307–4330MathSciNetMATHCrossRef
9.
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):499–507MathSciNetMATHCrossRef
10.
Wang X, Ding F (2015) Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle. Signal Process 117:208–218CrossRef
11.
Chen H, Ding F (2015) Hierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systems. Circuits Syst Signal Process 34(1):61–75MathSciNetMATHCrossRef
12.
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):1745–1755MATHCrossRef
13.
14.
Raja MAZ, Chaudhary NI (2014) Adaptive strategies for parameter estimation of Box-Jenkins systems. IET Signal Process 8(9):968–980CrossRef
15.
Chaudhary NI, Raja MAZ (2015) Design of fractional adaptive strategy for input nonlinear Box-Jenkins systems. Signal Process 116:141–151CrossRef
16.
Chaudhary NI, Raja MAZ (2015) Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms. Nonlinear Dyn 79(2):1385–1397MathSciNetMATHCrossRef
17.
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
18.
Bao B, Xu Y, Sheng J, Ding R (2011) Least squares based iterative parameter estimation algorithm for multivariable controlled ARMA system modelling with finite measurement data. Math Comput Model 53(9):1664–1669MathSciNetMATHCrossRef
19.
Yao G, Ding R (2012) Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average (CARMA) systems. Comput Math Appl 63(5):975–984MathSciNetMATHCrossRef
20.
Li J, Ding F (2015) Filtering-based recursive least-squares identification algorithm for controlled autoregressive moving average systems using the maximum likelihood principle. J Vib Control 21(15):3098–3106MathSciNetCrossRef
21.
Raja MAZ, Chaudhary NI (2015) Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems. Signal Process 107:327–339CrossRef
22.
Raja MAZ, Khan MAR, Mahmood T, Farooq U, Chaudhary NI (2016) Design of bio-inspired computing technique for nanofluidics based on nonlinear Jeffery–Hamel flow equations. Can J Phys 94(5):474–489CrossRef
23.
Chiroma H, Khan A, Abubakar AI, Saadi Y, Hamza MF, Shuib L, Gital AY, Herawan T (2016) A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm. Appl Soft Comput 48:50–58CrossRef
24.
Abubakar AI, Khan A, Nawi NM, Rehman MZ, Wah TY, Chiroma H, Herawan T (2016) Studying the effect of training levenberg marquardt neural network by using hybrid meta-heuristic algorithms. J Comput Theor Nanosci 13(1):450–460CrossRef
25.
Mall S, Chakraverty S (2015) Numerical solution of nonlinear singular initial value problems of Emden–Fowler type using Chebyshev Neural Network method. Neurocomputing 149:975–982CrossRef
26.
Draa A, Benayad Z, Djenna FZ (2015) An opposition-based firefly algorithm for medical image contrast enhancement. Int J Inf Commun Technol 7(4–5):385–405
27.
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
28.
Draa A, Bouaziz A (2014) An artificial bee colony algorithm for image contrast enhancement. Swarm Evol Comput 16:69–84CrossRef
29.
Raja MAZ, Samar R, Haroon T, Shah SM (2015) Unsupervised neural network model optimized with evolutionary computations for solving variants of nonlinear MHD Jeffery–Hamel problem. Appl Math Mech 36(12):1611–1638MathSciNetCrossRef
30.
Dahi ZAEM, Mezioud C, Draa A (2016) On the efficiency of the binary flower pollination algorithm: application on the antenna positioning problem. Appl Soft Comput 47:395–414CrossRef
31.
Raja MAZ, Samar R, Alaidarous ES, Shivanian E (2016) Bio-inspired computing platform for reliable solution of Bratu-type equations arising in the modeling of electrically conducting solids. Appl Math Model 40(11):5964–5977MathSciNetCrossRef
32.
Abubakar AI, Shuib L, Chiroma H (2015) Optimization of neural network using cuckoo search for the classification of diabetes. J Comput Theor Nanosci 12(12):5755–5758CrossRef
33.
Baymani M, Effati S, Niazmand H, Kerayechian A (2015) Artificial neural network method for solving the Navier–Stokes equations. Neural Comput Appl 26(4):765–773CrossRef
34.
Draa A, Bouzoubia S, Boukhalfa I (2015) A sinusoidal differential evolution algorithm for numerical optimisation. Appl Soft Comput 27:99–126CrossRef
35.
Effati S, Mansoori A, Eshaghnezhad M (2015) An efficient projection neural network for solving bilinear programming problems. Neurocomputing 168:1188–1197CrossRef
36.
37.
Raja MAZ, Shah FH, Alaidarous ES, Syam MI (2017) Design of bio-inspired heuristic technique integrated with interior-point algorithm to analyze the dynamics of heartbeat model. Appl Soft Comput 52:605–629CrossRef
38.
39.
40.
Ahmad I, Raja MAZ, Bilal M, Ashraf F (2016) Bio-inspired computational heuristics to study Lane–Emden systems arising in astrophysics model. SpringerPlus 5(1):1866CrossRef
41.
Raja MAZ, Abbas S, Syam MI, Wazwaz AM (2018) Design of neuro-evolutionary model for solving nonlinear singularly perturbed boundary value problems. Appl Soft Comput 62:373–394CrossRef
42.
Raja MAZ, Manzar MA, Shah FH, Shah FH (2018) Intelligent computing for Mathieu’s systems for parameter excitation, vertically driven pendulum and dusty plasma models. Appl Soft Comput 62:359–372CrossRef
43.
44.
Akbar S, Raja MAZ, Zaman F, Mehmood T, Khan MAR (2017) Design of bio-inspired heuristic techniques hybridized with sequential quadratic programming for joint parameters estimation of electromagnetic plane waves. Wirel Pers Commun 96(1):1475–1494CrossRef
45.
Raja MAZ, Azad S, Shah SM (2017) Bio-inspired computational heuristics to study the boundary layer flow of the Falkner–Scan system with mass transfer and wall stretching. Appl Soft Comput 57:293–314CrossRef
46.
47.
Raja MAZ, Samar R, Manzar MA, Shah SM (2017) Design of unsupervised fractional neural network model optimized with interior point algorithm for solving Bagley-Torvik equation. Math Comput Simul 132:139–158MathSciNetCrossRef
48.
Sabouri J, Effati S, Pakdaman M (2017) A neural network approach for solving a class of fractional optimal control problems. Neural Process Lett 45(1):59–74CrossRef
49.
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
50.
51.
Valarmathi K, Devaraj D, Radhakrishnan TK (2009) Real-coded genetic algorithm for system identification and controller tuning. Appl Math Model 33(8):3392–3401CrossRef
52.
Boudjelaba K, Ros F, Chikouche D (2014) Potential of particle swarm optimization and genetic algorithms for FIR filter design. Circuits Syst Signal Process 33(10):3195–3222CrossRef
53.
Arabali A, Ghofrani M, Etezadi-Amoli M, Fadali MS, Baghzouz Y (2013) Genetic-algorithm-based optimization approach for energy management. IEEE Trans Power Deliv 28(1):162–170CrossRef
54.
Nikolos IK, Valavanis KP, Tsourveloudis NC, Kostaras AN (2003) Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Trans Syst Man Cybern B (Cybern) 33(6):898–912CrossRef
55.
Dahi ZAEM, Mezioud C, Draa A (2016) A quantum-inspired genetic algorithm for solving the antenna positioning problem. Swarm Evol Comput 31:24–63CrossRef
56.
57.
58.
59.
60.
Masood Z, Majeed K, Samar R, Raja MAZ (2017) Design of Mexican Hat Wavelet neural networks for solving Bratu type nonlinear systems. Neurocomputing 221:1–14CrossRef
61.
Raja MAZ, Zameer A, Khan AU, Wazwaz AM (2016) A new numerical approach to solve Thomas-Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programming. SpringerPlus 5(1):1400CrossRef
62.
Raja MAZ, Farooq U, Chaudhary NI, Wazwaz AM (2016) Stochastic numerical solver for nanofluidic problems containing multi-walled carbon nanotubes. Appl Soft Comput 38:561–586CrossRef
63.
Özmen A, Weber GW (2014) RMARS: robustification of multivariate adaptive regression spline under polyhedral uncertainty. J Comput Appl Math 259:914–924MathSciNetMATHCrossRef
64.
Özmen A, Kropat E, Weber GW (2017) Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty. Optimization 66(12):2135–2155MathSciNetMATHCrossRef
65.
66.
67.
Taylan P, Weber GW, Yerlikaya F (2008) May. Continuous optimization applied in MARS for modern applications in finance, science and technology. In: ISI Proceedings of 20th mini-EURO conference continuous optimization and knowledge-based technologies, pp 317–322
68.
Weber GW, Batmaz İ, Köksal G, Taylan P, Yerlikaya-Özkurt F (2012) CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization. Inverse Probl Sci Eng 20(3):371–400MathSciNetMATHCrossRef
Metadata
Title
Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems
Authors
Ammara Mehmood
Aneela Zameer
Muhammad Asif Zahoor Raja
Rabia Bibi
Naveed Ishtiaq Chaudhary
Muhammad Saeed Aslam
Publication date
05-03-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3406-4

Other articles of this Issue 10/2019

Neural Computing and Applications 10/2019 Go to the issue

Original Article

A bargaining game model for performance assessment in network DEA considering sub-networks: a real case study in banking

Original Article

A novel double incremental learning algorithm for time series prediction

Original Article

A hybrid wavelet–APSO–ANN-based protection scheme for six-phase transmission line with real-time validation

Original Article

Bag-of-visual-words for signature-based multi-script document retrieval

Original Article

A neural dynamic system for solving convex nonlinear optimization problems with hybrid constraints

Original Article

A comparative analysis of ANN and chaotic approach-based wind speed prediction in India

Premium Partner

    Image Credits