References

• 1. 1)
     • A. Ratnaweera , S.K. Halgamuge , H.C. Watson . Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. , 240 - 255
  2. 2)
     • Sag, T., Cunkas, M.: `Multiobjective genetic estimation to induction motor parameters', Proc. Int. Aegean Conf. Elect. Mach. and Power Electron., ACEMP ’07 & Electromotion ’07, 10–12 September 2007, Bodrum, Turkey, p. 628–631.
  3. 3)
     • K.S. Huang , Q.H. Wu . Effective identification of induction motor parameters based on fewer measurements. IEEE Trans. Energy Convers. , 55 - 60
  4. 4)
     • Arumugam, M.S., Chandramohan, A., Rao, M.V.C.: `Competitive approaches to PSO algorithms via new acceleration co-efficient variant with mutation operators', Proc. Sixth Int. Conf. Computational Intelligent and Multimedia Application, ICCIMA ’05, 16–18 August 2005, p. 225–230.
  5. 5)
     • Meng, H.J., Zheng, P., Wu, R.Y., Hao, X.J., Xie, Z.: `A hybrid particle swarm algorithm with embedded chaotic search', Proc. 2004 IEEE Conf. Cybernate and Intelligent System, 1–3 December 2004, Singapore, p. 367–371.
  6. 6)
     • Feng, Y., Yao, Y.M., Wang, A.X.: `Comparing with chaotic inertia weights in particle swarm optimization', Proc. Sixth Int. Conf. Machine Learning and Cybernetics, 19–22 August 2007, Hong Kong, p. 329–333.
  7. 7)
     • Picardi, C., Rogano, N.: `Parameter identification of induction motor based on particle swarm optimization', Int. Symp. Power Electronic, Electrical Drives, Automation, and Motion, SPEEDAM 2006, 2006, p. 32–37.
  8. 8)
     • Feng, Y., Teng, G.F., Wang, A.X., Yao, Y.M.: `Chaotic inertia weight in particle swarm optimization', Second Int. Conf. Innovative Computing, Information and Control, ICICIC ’07, 5–7 September 2007, p. 475–478.
  9. 9)
     • Karimi, A., Choudhry, M.A., Feliachi, A.: `PSO-based evolutionary optimization for parameter identification of an induction motor', 39thNorth American Power Symp., NAPS 2007, 2007, p. 659–664.
  10. 10)
      • Bishop, R.R., Richards, G.G.: `Identifying induction machine parameters using a genetic optimization algorithm', Proc. IEEE, Southeastcon ’90, 1990, p. 476–479.
  11. 11)
      • B. Alatas , E. Akin , A.B. Ozer . Chaos embedded particle swarm optimization algorithms. J. Chaos, Solitons Fractals , 4 , 1715 - 1734
  12. 12)
      • Shi, Y., Eberhart, R.: `A modified particle swarm optimizer', Proc. IEEE Int. Conf. Evolution Computation, 4–9 May 1998, Piscataway, New Jersey, p. 69–73.
  13. 13)
      • Guangyi, C., Wei, G., Kaisheng, H.: `On line parameter identification of an induction motor using improved particle swarm optimization', Proc. 26th Chinese Control Conf., CCC 2007, July 2007, Zhangjiajie, Hunan, China, p. 745–749.
  14. 14)
      • Bergh, F.V.D.: `An analysis of particle swarm optimizers', 2001, PhD, Pretoria University, South Africa.
  15. 15)
      • S. Ayasun , C.O. Nwankpa . Induction motor tests using MATLAB/Simulink and their integration into undergraduate electric machinery courses. IEEE Trans. Educ. , 37 - 46
  16. 16)
      • Proco, A.B., Keyhani, A.: `Induction motor parameter identification from operating data electric drive applications', Proc. 18th Digital Avionics System Conf., 1999, 24–29 October 1999, p. 1–6.
  17. 17)
      • B. Liu , L. Wang , Y.H. Jin , F. Tang , D.X. Huang . Improved particle swarm optimization combined with chaos. J. Chaos, Solitons Fractals , 5 , 1261 - 1271
  18. 18)
      • B.K. Bose . (2002) AC machines for drives, Modern Power Electronics and AC Drives.
  19. 19)
      • R.K. Ursem , P. Vadstrup . Parameter identification of induction motors using stochastic optimization algorithms. J. Appl. Soft Comput. , 1 , 49 - 64
  20. 20)
      • Kennedy, J., Eberhart, R.: `Particle swarm optimization', Proc. IEEE Int. Conf. Neural Networks, 1995, p. 1942–1948.