Skip to main content
SpringerLink
Log in

Performance Comparison of Variants of Hybrid FLANN-DE for Intelligent Nonlinear Dynamic System Identification

  • Conference paper
  • First Online:
Progress in Advanced Computing and Intelligent Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1198))

  • 472 Accesses

Abstract

This work presents the performance comparison of different variants of hybrid Functional Linked Artificial Neural Network (FLANN) structures and Differential Evolution (DE) algorithm (FLANN-DE) for intelligent nonlinear dynamic system identification. FLANN is single-layer artificial neural network structure having less computational complexity and preferred for online applications and DE being a derivative-free metaheuristic algorithm is used as a global optimization tool. System identification finds its application in direct modelling, channel identification and estimation, geological exploration, instrumentation and control. Direct modelling is based on adaptive filtering concept and can be developed as an optimization problem. The goal of direct modelling is to estimate a model and a set of system parameters by minimizing the prediction error between the actual system output and the model output. The identification problem involves the construction of an estimated model which generates the output which matches that of desired system output when subjected to the same input signal. In this present work, hybrid FLANN-DE is proposed for direct modelling of nonlinear dynamic systems and comparative analysis is carried out for different variants of FLANN structures such as Chebyshev FLANN (CFLANN), Legendre FLANN (LFLANN) and Trigonometric FLANN (TFLANN) in terms of performance, the speed of computation and accuracy of results.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Widrow B, Stearns SD (1985) Adaptive signal processing. Prentice-Hall, Englewood Cliffs, NJ, pp 193–404

    MATH  Google Scholar 

  2. Haykin S (1996) Adaptive filter theory. Prentice-Hall Inc., Upper Saddle River, NJ

    MATH  Google Scholar 

  3. Sjoberg J, Zhang Q et al (1995) Nonlinear black-box modeling in system identification: a unified overview. Automatica 31(12):1691–1724

    Article  MathSciNet  Google Scholar 

  4. Guidorzi RP (1975) Canonical structure in the identification of multivariable systems. Automatica 11:361–374

    Article  MathSciNet  Google Scholar 

  5. Overbeek JMV, Ljung L (1989) On-Line structure selection for multivariable state space models. Automatica 18(5):529–543

    Article  Google Scholar 

  6. Johansen, TA (2000) Multi-objective Identification of FIR models. Proc IFAC Symp Syst Identif. SYSID2000. 3:917–922

    Google Scholar 

  7. Chen S, Billings SA (1989) Representation of non-linear systems: the NARMAX model. Int J Control 49:1013–1032

    Article  Google Scholar 

  8. Price KV (1999) An introduction to differential evolution. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, London, pp 79–108

    Google Scholar 

  9. Swayamsiddha S, Behera S, Thethi H (2015) Blind identification of nonlinear MIMO system using differential evolution techniques and performance analysis of its variants. In: Proceedings of international conference on computational intelligence and networks. IEEE, pp 63–67

    Google Scholar 

  10. Swayamsiddha S, Mondal S, Thethi H (2013) Identification of nonlinear dynamic systems using differential evolution based update algorithms and chebyshev functional link artificial neural network. In: IET proceedings of the third international conference on computational intelligence and information technology, pp 508–513

    Google Scholar 

  11. Narendra KS, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4–27

    Article  Google Scholar 

  12. Chen S, Billings SA, Grant PM (1990) Nonlinear system identification using neural networks. Int J Contr 51(6):1191–1214

    Article  MathSciNet  Google Scholar 

  13. Pao YH, Phillips SM, Sobajic DJ (1992) Neural-net computing and intelligent control systems. Int J Contr 56(2):263–289

    Article  MathSciNet  Google Scholar 

  14. Hayakawa T, Haddad WM, Bailey JM, Hovakimyan N (2005) Passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems. IEEE Trans Neural Netw 16(2):387–398

    Article  Google Scholar 

  15. Patra JC, Pal RN, Chatterji BN, Panda G (1999) Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B Cybern 29(2):254–262

    Article  Google Scholar 

  16. Yamamoto Y (2008) Identification of nonlinear discrete-time systems using trigonometric polynomial neural networks. In: international conference on control, automation and systems 2008 in COEX. Seoul, Korea, pp 366–370

    Google Scholar 

  17. Yamamoto, Y (2010) Identification of nonlinear systems using a trigonometric polynomial neural network. In: Proceedings of the international conference on modelling, identification and control. Okayama, Japan, pp. 35–40

    Google Scholar 

  18. Norouzi M, Mansouri M, Teshnehlab M, Shoorehdeli MA (2011) A novel type of trigonometric neural network trained by extended Kalman Filter. Fourth International Workshop on Advanced Computational Intelligence, Wuhan, Hubei, China, pp 590–595

    Google Scholar 

  19. Paraskevopoulos PN (1985) Legendre series approach to identification and analysis of linear systems. IEEE Trans Autom Control 30(6):585–589

    Article  MathSciNet  Google Scholar 

  20. Patra JC, Meher PK, Chakraborty G (2008) Development of intelligent sensors using legendre functional-link artificial neural networks. In: IEEE international conference on systems, man and cybernetics. pp 1140–1145

    Google Scholar 

  21. Zongzhun Z, Ye Y, Yongji W, Fuqiang X (2010) Entry trajectory genetic algorithm optimization using legendre pseudospectral method. In: Proceedings of the international conference on modelling, identification and control. Okayama, Japan, pp 505–510

    Google Scholar 

  22. Patra JC, Kot AC (2002) Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans Syst Man Cybern Part B Cybern 4:505–511

    Google Scholar 

  23. Yang L, Liu J, Yan R, Chen X (2019) Spline adaptive filter with arctangent-momentum strategy for nonlinear system identification. Sig Process 164:99–109

    Article  Google Scholar 

  24. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359

    Article  MathSciNet  Google Scholar 

  25. Lu L, Yu Y, Yang X, Wu W (2019) Time delay Chebyshev functional link artificial neural network. Neurocomputing 329:153–164

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, Kalinga Institute of Industrial Technology, KIIT University, Bhubaneswar, India

    Swati Swayamsiddha

Authors
  1. Swati Swayamsiddha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swati Swayamsiddha .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Chhabi Rani Panigrahi

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India

    Bibudhendu Pati

  3. Department of Computer Science, University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Swayamsiddha, S. (2021). Performance Comparison of Variants of Hybrid FLANN-DE for Intelligent Nonlinear Dynamic System Identification. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1198. Springer, Singapore. https://doi.org/10.1007/978-981-15-6584-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-6584-7_14

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6583-0

  • Online ISBN: 978-981-15-6584-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation