Skip to main content
SpringerLink
Log in
Book cover

Chaos, Complexity and Leadership 2012 pp 177–181Cite as

IDEAS The Modelling Technique Based on Neuro-Fuzzy Structure for Chaotic Rossler System

  • Conference paper
  • First Online:

  • 1828 Accesses

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Abstract

In this study, the modelling and simulation of chaotic Rossler system has been carried out by using an intelligent system based on neuro-fuzzy modelling technique. Furthermore, the MATLAB simulation of chaotic Rossler system has been carried out for comparison with proposed technique. Structure of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is improved and trained in MATLAB toolbox. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS network. Numerical simulations are used in this study. We have used four various data sets for testing the simulation speed of ANFIS and MATLAB. Obtained Results show that the proposed modelling technique has much higher speed and accuracy in comparison with MATLAB simulation. The neuro-fuzzy modelling technique can be simply used in software tools for designing and simulation of the chaotic Rossler system and the other chaotic systems.

This is a preview of subscription content, 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
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  • Andrejevic M, Litovski V (2003) Electronic circuit modeling using artificial neural network. J Autom Control 13:31–37

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  • Culliere T, Titli A, Corrieu JM (1995) Neuro-fuzzy modelling of nonlinear systems for control purposes. In: Proceedings of the IEEE international conference on fuzzy systems, vol 4, pp 2009–2016

    Google Scholar 

  • Djeffal F, Chahdi M (2007) An approach based on neural computation to simulate the nanoscale CMOS circuits: application to the simulation of CMOS inverter. Elsevier Solid-State Electron 51:48–56

    Article  ADS  Google Scholar 

  • Fotsin HB, Woafo P (2005) Adaptive synchronization of a modified and uncertain chaotic Van der Pol–Duffing oscillator based on parameter identification. Chaos Solut Fract 24:1363–1371

    Article  MathSciNet  ADS  MATH  Google Scholar 

  • Fotsin H, Bowong S, Daafouz J (2005) Adaptive synchronization of two chaotic systems consisting of modified Van der Pol–Duffing and Chua oscillators. Chaos Solut Fract 26:215–229

    Article  MathSciNet  ADS  MATH  Google Scholar 

  • Hayati M, Rezaei A, Seifi M et al (2010) Modeling and simulation of combinational CMOS logic circuits by ANFIS. Microelectron J 41:381–387

    Article  Google Scholar 

  • Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE Spec Sueon Fuzzy Logic Eng Appl 83:378–406

    Google Scholar 

  • Li HB, Sun ZQ, Min HB et al (2011) Fuzzy dynamic characteristic modeling and adaptive control of nonlinear systems and its application to hypersonic vehicles. Sci China Inf Sci 54:460–468

    Article  MathSciNet  MATH  Google Scholar 

  • Lü J, Chen G (2002) A new chaotic attractor coined. Int J Bifurc Chaos 12:659–661

    Article  MATH  Google Scholar 

  • Moaddy K, Hashim I, Momani S (2011) Non-standard finite difference schemes for solving fractional-order Rossler chaotic and hyperchaotic systems. Comput Math Appl 62:1068–1074

    Article  MathSciNet  MATH  Google Scholar 

  • Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12:145–151

    Article  Google Scholar 

  • Rossler OE (1979) An equation for hyperchaos. Phys Lett A 71:155–157

    Article  MathSciNet  ADS  Google Scholar 

  • Shoorehdeli MA, Teshnehlab M, Sedigh AK et al (2009) Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods. Appl Soft Comput 9:833–850

    Article  Google Scholar 

  • Soyguder S, Alli H (2009) An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modeling Approach. Energy Build 41:814–822

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEET Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  • Turkmen I, Guney K (2005) Computation of association probabilities for single target tracking with the use of adaptive neuro-fuzzy inference system. Turk J Electr Eng Comput Sci 13:105–118

    Google Scholar 

  • Wang FQ, Liu CX (2006) Synchronization of hyperchaotic Lorenz system based on passive control. Chin Phys 15:1971–1975

    Article  ADS  Google Scholar 

  • Wu X, Wang H, Lu H (2012) Modified generalized projective synchronization of a new fractional-order hyperchaotic system and its application to secure communication. Nonlin Anal Real World Appl 13:1441–1450

    Article  MathSciNet  MATH  Google Scholar 

  • Yu X (1997) Variable structure control approach for controlling chaos. Chaos, Solitons, Fractals, 8(9):1577

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electronic and Communication Technologies, Yuzuncu Yil University, Ercis Technical Vocational School of Higher Education, Ercis, Van, 65080, Turkey

    Remzi Tuntaş

Authors
  1. Remzi Tuntaş
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Remzi Tuntaş .

Editor information

Editors and Affiliations

  1. International Science Association, Ankara, Turkey

    Santo Banerjee

  2. International Science Association, Ankara, Turkey

    Şefika Şule Erçetin

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this paper

Cite this paper

Tuntaş, R. (2014). IDEAS The Modelling Technique Based on Neuro-Fuzzy Structure for Chaotic Rossler System. In: Banerjee, S., Erçetin, Ş. (eds) Chaos, Complexity and Leadership 2012. Springer Proceedings in Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7362-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-7362-2_22

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-7361-5

  • Online ISBN: 978-94-007-7362-2

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

Publish with us

Policies and ethics

Search

Navigation