Skip to main content
SpringerLink
Log in

Parameter identification of nonlinear system using an improved Lozi map based chaotic optimization algorithm (ILCOA)

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

In this paper, an efficient stochastic optimization algorithm is presented for parameter identification of nonlinear systems. Due to its robust performance, short running time and desirable potency to find local minimums the Lozi map-based chaotic optimization algorithm is an appropriate choice to estimate unknown parameters of nonlinear dynamic systems. To enhance the identification efficacy and in order to escape local minimum, a modified version of this algorithm with higher stability and better performance is rendered in this paper. An Improved Lozi map-based chaotic optimization algorithm (ILCOA) is employed to identify three nonlinear systems and the performance of the proposed algorithm is compared with other optimization algorithms. The simulation results of identification endorse the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  • Acharjee P, Goswami SK (2010) Chaotic particle swarm optimization based robust load flow. Int J Electr Power Energy Syst 32(2):141–146

    Article  Google Scholar 

  • Angelov P, Yager R (2013) Density-based averaging—a new operator for data fusion. Inf Sci 222:163–174

    Article  MathSciNet  Google Scholar 

  • Angelov P, Ramezani R, Zhou X (2008) Autonomous novelty detection and object tracking in video streams using evolving clustering and Takagi-Sugeno type neuro-fuzzy system. In: IEEE international joint conference on neural networks, 2008. IJCNN 2008 (IEEE World Congress on Computational Intelligence). pp 1456–1463. IEEE

  • Angelov P, Škrjanc I, Blažič S (2013) Robust evolving cloud-based controller for a hydraulic plant. In: 2013 IEEE conference on evolving and adaptive intelligent systems (EAIS), pp 1–8. IEEE

  • Azami H, Malekzadeh M, Sanei S, Khosravi A (2012) Optimization of orthogonal polyphase coding waveform for MIMO radar based on evolutionary algorithms. J Math Comput Sci 6(2):146–153

    Article  Google Scholar 

  • Babu BC, Gurjar S (2014) A novel simplified two-diode model of photovoltaic (PV) module. IEEE J Photovolt 4(4):1156–1161

    Article  Google Scholar 

  • Billings SA, Jamaluddin HB, Chen S (1991) A comparison of the backpropagation and recursive prediction error algorithms for training neural networks. Mech Syst Signal Process 5(3):233–255

    Article  Google Scholar 

  • Bresler Y, Macovski A (1986) Exact maximum likelihood parameter estimation of superimposed exponential signals in noise. IEEE Trans Acoust Speech Signal Process 34(5):1081–1089

    Article  Google Scholar 

  • Chatterjee A, Ghoshal SP, Mukherjee V (2011) Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer. Int J Electr Power Energy Syst 33(3):657–672

    Article  Google Scholar 

  • Chellaswamy C, Ramesh R (2016) Parameter extraction of solar cell models based on adaptive differential evolution algorithm. Renew Energy 97:823–837

    Article  Google Scholar 

  • Chen G, Ueta T (1999) Yet another chaotic attractor. Int J Bifur Chaos 9(07):1465–1466

    Article  MathSciNet  Google Scholar 

  • Chen S, Billings SA, Luo W (1989) Orthogonal least squares methods and their application to non-linear system identification. Int J Control 50(5):1873–1896

    Article  Google Scholar 

  • Costa B, Skrjanc I, Blazic S, Angelov P (2013) A practical implementation of self-evolving cloud-based control of a pilot plant. In: 2013 IEEE international conference on cybernetics (CYBCONF), pp 7–12. IEEE

  • dos Santos Coelho L (2009) Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach. Chaos Solitons Fractals 39(4):1504–1514

    Article  Google Scholar 

  • Farahani M, Ganjefar S, Alizadeh M (2012) PID controller adjustment using chaotic optimisation algorithm for multi-area load frequency control. IET Control Theory Appl 6(13):1984–1992

    Article  MathSciNet  Google Scholar 

  • Gao X, Cui Y, Hu J, Xu G, Wang Z, Qu J, Wang H (2018) Parameter extraction of solar cell models using improved shuffled complex evolution algorithm. Energy Convers Manag 157:460–479

    Article  Google Scholar 

  • Godfrey KR, Jones P (1986) Signal processing for control, vol 79. Springer, Berlin

    Book  Google Scholar 

  • Gong W, Cai Z (2013) Parameter extraction of solar cell models using repaired adaptive differential evolution. Sol Energy 94:209–220

    Article  Google Scholar 

  • Hejri M, Mokhtari H, Azizian MR, Ghandhari M, Soder L (2014) On the parameter extraction of a five-parameter double-diode model of photovoltaic cells and modules. IEEE J Photovolt 4(3):915–923

    Article  Google Scholar 

  • Ishaque K, Salam Z, Taheri H (2011) Simple, fast and accurate two-diode model for photovoltaic modules. Solar Energy Mater Solar Cells 95(2):586–594

    Article  Google Scholar 

  • Jaleel EA, Aparna K (2018) Identification of realistic distillation column using hybrid particle swarm optimization and NARX based artificial neural network. Evol Syst 1–18

  • Li X, Yin M (2014) Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dyn 77(1–2):61–71

    Article  MathSciNet  Google Scholar 

  • Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20(2):130–141

    Article  MathSciNet  Google Scholar 

  • Malekzadeh M, Khosravi A, Alighale S, Azami H (2012) Optimization of orthogonal poly phase coding waveform based on bees algorithm and artificial bee colony for mimo radar. In: International conference on intelligent computing. Springer, Berlin, Heidelberg, pp 95–102

    Google Scholar 

  • Malekzadeh M, Sadati J, Alizadeh M (2016) Adaptive PID controller design for wing rock suppression using self-recurrent wavelet neural network identifier. Evol Syst 7(4):267–275

    Article  Google Scholar 

  • Malekzadeh M, Khosravi A, Tavan M (2018a) Observer based control scheme for DC-DC boost converter using sigma–delta modulator. COMPEL Int J Comput Math Electr Electron Eng 37(2):784–798

    Article  Google Scholar 

  • Malekzadeh M, Khosravi A, Tavan M (2018b) Immersion and invariance-based filtered transformation with application to estimator design for a class of DC–DC converters. Trans Inst Meas Control 0142331218777563

  • Mendel E, Krohling RA, Campos M (2011) Swarm algorithms with chaotic jumps applied to noisy optimization problems. Inf Sci 181(20):4494–4514

    Article  MathSciNet  Google Scholar 

  • Niu Q, Zhang H, Li K (2014) An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int J Hydrogen Energy 39(8):3837–3854

    Article  Google Scholar 

  • Sadeghi-Tehran P, Cara AB, Angelov P, Pomares H, Rojas I, Prieto A (2012) Self-evolving parameter-free rule-based controller. In 2012 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–8. IEEE

  • Salahshour E, Malekzadeh M, Gordillo F, Ghasemi J (2018a) Quantum neural network-based intelligent controller design for CSTR using modified particle swarm optimization algorithm. Trans Inst Meas Control 0142331218764566

  • Salahshour E, Malekzadeh M, Gholipour R, Khorashadizadeh S (2018b) Designing multi-layer quantum neural network controller for chaos control of rod-type plasma torch system using improved particle swarm optimization. Evol Syst 1–15

  • Tavazoei MS, Haeri M (2007) Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms. Appl Math Comput 187(2):1076–1085

    MathSciNet  MATH  Google Scholar 

  • Ursem RK, Vadstrup P (2004) Parameter identification of induction motors using stochastic optimization algorithms. Appl Soft Comput 4(1):49–64

    Article  Google Scholar 

  • Wang,J.,Chen,X.andFu,J., 2014.Adaptive finite-time control of chaos in permanent magnet synchronous motor with uncertain parameters.Nonlinear Dyn 78(2):1321–1328

    Article  Google Scholar 

  • Xiong G, Zhang J, Shi D, He Y (2018) Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm. Energy Convers Manag 174:388–405

    Article  Google Scholar 

  • Xu S, Wang Y (2017) Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Convers Manag 144:53–68

    Article  Google Scholar 

  • Zheng YX, Liao Y (2016) Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization. Optik-Int J Light Electron Opt 127(19):7865–7874

    Article  Google Scholar 

  • Zhou X, Angelov P, 2007, April. Autonomous visual self-localization in completely unknown environment using evolving fuzzy rule-based classifier. In: IEEE Symposium on computational intelligence in security and defense applications, 2007. CISDA 2007, pp 131–138. IEEE

  • Zhou CS, Chen TL (2000) Chaotic neural networks and chaotic annealing. Neurocomputing 30(1–4):293–300

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran

    S. Mohammadreza Ebrahimi & Milad Malekzadeh

  2. Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran

    Mojtaba Alizadeh

  3. Department of Precision and Microsystems Engineering, Delft University of Technology, Delft, The Netherlands

    S. Hassan HosseinNia

Authors
  1. S. Mohammadreza Ebrahimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milad Malekzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mojtaba Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Hassan HosseinNia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milad Malekzadeh.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ebrahimi, S.M., Malekzadeh, M., Alizadeh, M. et al. Parameter identification of nonlinear system using an improved Lozi map based chaotic optimization algorithm (ILCOA). Evolving Systems 12, 255–272 (2021). https://doi.org/10.1007/s12530-019-09266-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-019-09266-9

Keywords

Search

Navigation