SOS-BASED BLIND IDENTIFICATION OF NONLINEAR VOLTERRA SYSTEMS

H.-Z. Tan, T. Aboulnasr, and J. Fu

References

  1. [1] V.J. Mathews & G.L. Sicuranza, Polynomial signal processing(New York: John Wiley & Sons, 2000).
  2. [2] J.S. Bendat, Nonlinear system techniques and applications(New York: Wiley, 1998).
  3. [3] M. Schetzen, The Volterra and Wiener theories of nonlinearsystems (New York: Wiley, 1980).
  4. [4] A.K. Nandi (Ed.), Blind estimation using higher-order statistics(Boston: Kluwer Academic, 1999).
  5. [5] K. Abed-Meraim, W. Qiu, & Y. Hua, Blind system identifica-tion, Proceedings of the IEEE, 85, 1997, 1310–1322.
  6. [6] A. Borys, Nonlinear aspects of telecommunications (Florida:CRC, 2001).
  7. [7] G.L. Sicuranza, Quadratic filters for signal processing, Pro-ceedings of the IEEE, 80, 1992, 1263–1285.
  8. [8] C. Jaszczur & T. Aboulnasr, Low complexity, real exponenthigh order polynomial filters, IEEE International Symposiumon Signal Processing and Information Technology, Morocco,December 2002, 648–652.
  9. [9] H.-Z. Tan & N. Sepehri, Parametric fault diagnosis for electro-hydraulic cylinder drive units, IEEE Transactions on IndustrialElectronics, 49, 2002, 96–106.
  10. [10] J. Chen & R.J. Patton, Robust model-based fault diagnosis fordynamic systems (Boston, MA: Kluwer Academic, 1999).
  11. [11] L. Tan & J. Jiang, Adaptive Volterra filters for active controlof nonlinear noise processes, IEEE Transaction on SignalProcessing, 49, 2001, 1667–1676.
  12. [12] K. Kim, S.B. Kim, E.J. Powers, R.W. Miksad, & F.J. Fischer,Adaptive second-order Volterra filtering and its applicationto second-order drift phenomena, IEEE Journal of OceanicEngineering, 19, 1994, 183–192.
  13. [13] K.H. Chon, Y.-M. Chen, N.-H. Holstein-Rathlou, & V.Z.Marmarelis, Nonlinear system analysis of renal autoregulationin normotensive and hypertensive rats, IEEE Transactions onBiomedical Engineering, 45, 1998, 342–353.
  14. [14] V.Z. Marmarelis, Identification of nonlinear biological systemsusing Laguerre expansion of kernels, Annals of BiomedicalEngineering, 21, 1993, 673–689.
  15. [15] J.G. Nemeth & J. Schoukens, Efficient identification of thirdorder Volterra models using interpolation techniques, IEEEConf. Instrumentation and Measurement Technology, Anchor-age, USA, May 2002, 1119–1124.
  16. [16] H.-Z. Tan & T.W.S. Chow, Blind identification of quadraticnonlinear models using neural networks with higher ordercumulants, IEEE Transcations on Industrial Electronics, 47,June 2000, 687–696.
  17. [17] N. Petrochilos & P. Comon, Blind identification of linear-quadratic channels with usual communication inputs, Proc.Tenth IEEE Workshop on Statistical Signal and Array Pro-cessing, August 2000, 181–185.
  18. [18] G.A. Glentis, P. Koukoulas, & N. Kalouptsidis, Efficient algo-rithm for Volterra system idenitifcation, IEEE Transactionson Signal Processing, 47, 1999, 3042–3057.
  19. [19] L. Tan & J. Jiang, An adaptive techniques for modeling second-order Volterra systems with sparse kernels, IEEE Transactionson Signal Processing, 45, 1998, 1610–1615.
  20. [20] P. Bondon, Blind identifiability of a quadratic stochastic sys-tem, IEEE Transactions on Information Theory, 41, 1995,245–254.
  21. [21] A.M. Zoubir, Identification of quadratic Volterra systems drivenby non-Gaussian processes, IEEE Transactions on Signal Pro-cessing, 43, 1995, 1302–1306.
  22. [22] S.W. Nam & E.J. Powers, Application of higher order spectralanalysis to cubically nonlinear system identification, IEEETransactions on Signal Processing, 42, 1994, 1746–1765.
  23. [23] H.-Z. Tan & T. Aboulnasr, Blind identifiability of third-orderVolterra nonlinear systems, Proc. ICASSP, Hong Kong, April2003, 665–668.
  24. [24] G.G. Giannakis & E. Serpedin, Linear multichannel blindequalizers of nonlinear FIR Volterra channels, IEEE Transac-tions on Signal Processing, 45, 1997, 67–81.
  25. [25] C. Nikias & A. Petropulu, Higher-order spectra analysis (NewJersey: Prentice Hall, 1993).
  26. [26] G.B. Giannakis & J. M. Mendel, Identification of nonminimumphase systems using higher order statistics, IEEE Transactionson Acoustic, Speech and Signal Processing, 37, 1989, 360–377.
  27. [27] T. Aboulnasr & K. Mayyas, Complexity reduction of the NLMSalgorithm via selective coefficient update, IEEE Transactionson Signal Processing, 47, 1999, 1421–1424.44
  28. [28] T. Aboulnasr & K. Mayyas, A robust variable step-size LMS-type algorithm: analysis and simulations, IEEE Transactionson Signal Processing, 45, 1997, 631–639.
  29. [29] R.L. Florom, Report on improved wayside train inspectionprogram (Chicago, Association of American Railroads, 1994).
  30. [30] R. Johansson, System modeling and identification (PrenticeHall, 1993).

Important Links:

Go Back