Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Identification of nonlinear systems by the genetic programming-based Volterra filter

Identification of nonlinear systems by the genetic programming-based Volterra filter

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Signal Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The genetic programming (GP) algorithm is utilised to search for the optimal Volterra filter structure. A Volterra filter with high order and large memories contains a large number of cross-product terms. Instead of applying the GP algorithm to search for all cross-products of input signals, it is utilised to search for a smaller set of primary signals that evolve into the whole set of cross-products. With GP's optimisation, the important primary signals and the associated cross-products of input signals contributing most to the outputs are chosen whereas the primary signals and the associated cross-products of input signals that are trivial to the outputs are excluded from the possible candidate primary signals. To improve GP's learning capability, an effective directed initialisation scheme, a tree pruning and reorganisation approach, and a new operator called tree extinction and regeneration are proposed. Several experiments are made to justify the effectiveness and efficiency of the proposed modified by the GP algorithm.

References

    1. 1)
      • V.J. Mathews , G.L. Sicuranza . (2000) Polynomial signal processing.
    2. 2)
      • G.P. Liu , V. Kadirkamanathan . Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms. IEE Proc. – Control Theory Appl. , 5 , 373 - 382
    3. 3)
      • N.Y. Nikolaev , H. Iba . Learning polynomial feedforward neural networks by genetic programming and backpropagation. IEEE Trans. Neural Netw. , 2 , 337 - 350
    4. 4)
      • L. Yao . Genetic algorithm based identification of nonlinear systems by sparse Volterra filters. IEEE Trans. Signal Process. , 12 , 3433 - 3435
    5. 5)
      • W. Banzhaf , P. Nordin , R. Keller , F. Francone . (1998) Genetic programming – an introduction on the automatic evolution of computer programs and its application.
    6. 6)
      • M.A. Syed , V.J. Mathews . Lattice algorithms for recursive least squares adaptive second-order Volterra filtering. IEEE Trans. Signal Process. , 3 , 202 - 214
    7. 7)
      • K.H. Chon , Y.-M. Chen , N.-H. Holstein-Rathlou , V.Z. Marmarelis . Nonlinear system analysis of renal autoregulation in normotensive and hypertensive rats. IEEE Trans. Biomed. Eng. , 3 , 342 - 353
    8. 8)
      • J. Lee , V.J. Mathews . A fast recursive least squares adaptive second-order Volterra filter and its performance analysis. IEEE Trans. Signal Process. , 3 , 1087 - 1102
    9. 9)
      • K. Witrisal , G. Leus , M. Pausini , C. Krall . Equivalent system model and equalization of differential impulse radio UWB systems. IEEE J. Selected Areas Commun. , 9 , 1851 - 1862
    10. 10)
      • G.M. Raz , B.D. Van Veen . Blind equalization and identification of nonlinear and IIR systems – a least squares approach. IEEE Trans. Signal Process. , 1 , 192 - 200
    11. 11)
      • D.W. Griffith , G.R. Arce . Partially decoupled Volterra filters: formulation and LMS adaptation. IEEE Trans. Signal Process. , 6 , 1485 - 1494
    12. 12)
      • G.-O.A. Glentis , P. Koukoulas , N. Kalouptsidis . Efficient algorithms for Volterra system identification. IEEE Trans. Signal Process. , 11 , 3042 - 3057
    13. 13)
      • F. Kuech , W. Kellermann . Partitioned block frequency-domain adaptive second-order volterra filter. IEEE Trans. Signal Process. , 2 , 564 - 575
    14. 14)
      • Louis, L. Scharf . (1991) Statistical signal processing.
    15. 15)
      • M. Sayadi , F. Fnaiech , M. Najim . An LMS adaptive second-order Volterra filter with a zeroth-order term: steady-state performance analysis in a time-varying environment. IEEE Trans. Signal Process. , 3 , 872 - 876
    16. 16)
      • T. Koh , J.E. Powers . Second-order Volterra filtering and its application to nonlinear system identification. IEEE Trans. Acoust. Speech Signal Process. , 1445 - 1455
    17. 17)
      • A. Guérin , G. Faucon , Regine Le Bouquin-Jeannès . Nonlinear acoustic echo cancellation based on Volterra filters. IEEE Trans. Speech Audio Process. , 6 , 672 - 683
    18. 18)
      • N. Kalouptsidis . (1997) Signal processing systems: theory and design.
    19. 19)
      • M. Sayadi , F. Fnaiech , M. Najim . Multichannel linear and quadratic adaptive filtering based on the Chandrasekhar fast algorithm. IEEE Trans. Signal Process. , 3 , 860 - 864
    20. 20)
      • B. Weng , K.E. Barner . Nonlinear system identification in impulsive environment. IEEE Trans. Signal Process. , 7 , 2588 - 2594
    21. 21)
      • G.L. Sicuranza , A. Carini . A multichannel hierarchical approach to adaptive Volterra filters employing filtered-X affine projection algorithms. IEEE Trans. Signal Process. , 4 , 1463 - 1473
    22. 22)
      • D.-C. Park , T.-K. Jung Jeong . Complex-bilinear recurrent neural network for equalization of a digital satellite channel. IEEE Trans. Neural Netw. , 3 , 711 - 725
    23. 23)
      • C. Eun , E.J. Powers . A new Volterra predistorter based on the indirect learning architecture. IEEE Trans. Signal Process. , 1 , 223 - 227
    24. 24)
      • J. Koza . (1994) Genetic programming II: automatic discovery of reusable program.
    25. 25)
      • A. Monin , G. Salut , V. Teuliere . Multisource discrimination using IIR Volterra filtering. IEEE Trans. Commun. , 5 , 922 - 931
    26. 26)
      • M.H. Asyali , M. Juusola . Use of Meixner functions in estimation of Volterra kernels of nonlinear systems with delay. IEEE Trans. Biomed. Eng. , 2 , 229 - 237
    27. 27)
      • A. Carini , E. Mumolo , G.L. Sicuranza . V-vector algebra and its application to Volterra-adaptive filtering. IEEE Trans. Signal Process. , 5 , 1585 - 1598
    28. 28)
      • D. Levanony , N. Berman . Recursive nonlinear system identification by a stochastic gradient algorithm: stability, performance, and model nonlinearity considerations. IEEE Trans. Signal Process. , 9 , 2540 - 2550
    29. 29)
      • B. Widrow , S.D. Stearns . (1985) Adaptive signal processing.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr_20070203
Loading

Related content

content/journals/10.1049/iet-spr_20070203
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address