Skip to main content
SpringerLink
Log in
Book cover

Applications of Soft Computing pp 251–260Cite as

Fuzzy Model Identification for Rapid Nickel-Cadmium Battery Charger through Particle Swarm Optimization Algorithm

  • Conference paper

  • 1029 Accesses

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 36))

Abstract

This paper presents the fuzzy model identification for rapid Nickel-Cadmium (Ni-Cd) battery charger by applying Particle Swarm Optimization (PSO) algorithm on the input-output data. Models generated through this approach provide the flexibility of black-box approach like neural networks, since it does not need to know any information regarding the process that generates the data. The PSO method is a member of the broad category of swarm intelligence techniques for finding optimized solutions. The motivation behind the PSO algorithm is the social behavior of animals viz. flocking of birds and fish schooling and has its origin in simulation for visualizing the synchronized choreography of bird flock. The data for the batteries charger was obtained through experimentation with an objective to charge the batteries as fast as possible. The implementation of the approach is described and simulation results are presented to illustrate its effectiveness.

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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arun Khosla, Shakti Kumar and K.K. Aggarwal (2002) Design and Development of RFC-10: A Fuzzy Logic Based Rapid Battery Charger for Nickel-Cadmium Batteries, HiPC2002 Workshop on Soft Computing, Bangalore, pp. 9–14.

    Google Scholar 

  2. Arun Khosla (1997) Design and Development of RFC-10: A Fuzzy Logic Based Rapid Battery Charger for Nickel-Cadmium Batteries. M.Tech. Thesis, Kurukshetra University, Kurukshetra. India.

    Google Scholar 

  3. H. Hellendoorn and D. Driankov (Eds.)(1997), Fuzzy Model Identification — Selected Approaches, Springer-Verlag.

    Google Scholar 

  4. John Yen and Reza Langari (2003) Fuzzy Logic — Intelligence, Control and Information, Pearson Education, First Indian Reprint.

    Google Scholar 

  5. A. Bastian (1996) A genetic algorithm for tuning membership functions, Fourth European Congress on Fuzzy and Intelligent Technologies EUFIT(96), Aachen, Germany, vol.1, pp. 494–498.

    Google Scholar 

  6. B. Carse, T.C. Fogarty and A. Munro (1996) Evolving fuzzy rule-based controllers using GA, Fuzzy Sets and Systems. 80:273–294.

    Article  Google Scholar 

  7. O. Nelles (1996) FUREGA-Fuzzy Rule Extraction by GA, Fourth European Congress on Fuzzy and Intelligent Technologies EUFIT(96), Aachen, Germany, vol. 1, 1996, pp. 489–493.

    Google Scholar 

  8. K. Nozaki, T. Morisawa, H. Ishibuchi (1995) Adjusting membership functions in fuzzy rule-based classication systems, Third European Congress on Fuzzy and Intelligent Technologies, EUFIT(95), Aachen, Germany, vol. 1, pp. 615–619.

    Google Scholar 

  9. M. Setnes, J.A. Roubos (1999) Transparent fuzzy modelling using clustering and GAs, NAFIPS Conference, June 10–12, New York, USA, pp.198–202.

    Google Scholar 

  10. Arun Khosla, Shakti Kumar and K.K. Aggarwal (2003) Identification of Fuzzy Controller for Rapid Nickel-Cadmium Batteries Charger through Fuzzy c-means Clustering Algorithm, Proceedings of NAFIPS(2003), Chicago, July 24–26, pp. 536–539.

    Google Scholar 

  11. Patricia Melin and Oscar Castillo (2005) Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system, Information Sciences, pp. 133–151.

    Google Scholar 

  12. Arun Khosla, Shakti Kumar and K.K. Aggarwal (2003) Fuzzy Controller for Rapid Nickel-Cadmium Batteries Charger through Adaptive Neuro-Fuzzy Inference System (ANFIS) Architecture, Proceedings of NAFIPS(2003), Chicago, July 24–26, pp. 540–544.

    Google Scholar 

  13. K.E. Parsopoulos and M.N. Vrahatis (2002) Recent approaches to global optimization problems through Particle Swarm Optimization, Natural Computing, Kluwer Academic Publishers, pp. 235–306.

    Google Scholar 

  14. Eberhart, R.C and Shi, Y. (2001) Particle Swarm Optimization: Developments, Applications and Resources, Proceedings of the Congress on Evolutionary Computation, Seoul, Korea, pp. 81–86.

    Google Scholar 

  15. J. Kennedy and R. Eberhart (1995), Particle Swarm Optimization, Proceedings of IEEE Conference on Neural Networks, vol. IV, Perth, Australia, pp. 1942–1948.

    Google Scholar 

  16. J. Kennedy and R. Eberhart (2001), Swarm Intelligence, Morgan Kaufmann Publishers.

    Google Scholar 

  17. Eberhart R.C. and Kennedy J (1995) A New Optimizer Using Particle Swarm Theory, Proceedings Sixth Symposium on Micro Machine and Human Science, IEEE Service Centre, Piscataway, NJ, pp. 39–43.

    Google Scholar 

  18. Y. Shi and R. Eberhart (2001) Fuzzy Adaptive Particle Swarm Optimization, IEEE International Conference on Evolutionary Computation, pp. 101–106.

    Google Scholar 

  19. Xiao-Feng Xie, Wen-Jun Zhang, Zhi-Lian Yang (2002) Adaptive particle swarm optimization on individual level, ICSP 2002, pp 1215–1218.

    Google Scholar 

  20. David Linden (Editor-in-Chief) (1995), Handbook of Batteries, McGraw Hill Inc.

    Google Scholar 

  21. Isidor Buchmann (1997) Getting the most out of your cell phone batteries, Express Telecom, May 16–31, pp. 22–23.

    Google Scholar 

  22. Sunny Wan (1996) The Chemistry of Rechargeable Batteries, Cadex Electronics Inc., Canada, Revised August 26, 1996.

    Google Scholar 

  23. Esmin, A. A. A., Aoki, A. R., and Lambert-Torres G. (2002) Particle swarm optimization for fuzzy membership functions optimization. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 108–113.

    Google Scholar 

  24. Jang, J.-S.R (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transactions on Systems, Man and Cybernetics, pp. 665–685.

    Google Scholar 

  25. K.K. Aggarwal, Shakti Kumar, Arun Khosla and Jagatpreet Singh (2003) Introducing Lifetime Parameter in Selection Based Particle Swarm Optimization for Improved Performance, First Indian International Conference on Artificial Intelligence, Hyderabad, India, pp. 1175–1181.

    Google Scholar 

  26. Odetayo M. O. (1997) Empirical Studies of Interdependencies of Genetic Algorithm Parameters, Proceedings of the 23rd EUROMICRO 97 Conference — New Frontiers of Information Technology, pp. 639–643.

    Google Scholar 

  27. David E Goldberg (2001) Genetic Algorithms in Search, Optimization and Machine Learning, Pearson Education Asia, New Delhi.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dr B R Ambedkar National Institute of Technology, Jalandhar, 144011, India

    Arun Khoslal

  2. Haryana Engineering College, Jagadhari, 135003, India

    Shakti Kumar

  3. GGS Indraprastha University, Delhi, 110006, India

    K. K. Aggarwal

  4. Infosys Technologies Limited, Chennai, 600019, India

    Jagatpreet Singh

Authors
  1. Arun Khoslal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shakti Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. K. Aggarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jagatpreet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Enterprise Integration School of Industrial and Manufacturing Science (SIMS), Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK

    Ashutosh Tiwari  & Rajkumar Roy  & 

  2. School of Chemistry, University of Manchester, Faraday Building, Sackville Street, PO Box 88, Manchester, M6o 1QD, UK

    Joshua Knowles

  3. Centre for Transport & Society Faculty of the Built Environment, University of the West of England, Frenchay Campus Coldharbour Lane, Bristol, BS16 1QY, UK

    Erel Avineri

  4. School of Informatics, University of Bradford, Richmond Road, Bradford, West Yorkshire, BD7 1DP, UK

    Keshav Dahal

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Khoslal, A., Kumar, S., Aggarwal, K.K., Singh, J. (2006). Fuzzy Model Identification for Rapid Nickel-Cadmium Battery Charger through Particle Swarm Optimization Algorithm. In: Tiwari, A., Roy, R., Knowles, J., Avineri, E., Dahal, K. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 36. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36266-1_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-36266-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29123-7

  • Online ISBN: 978-3-540-36266-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation