S. Chen, S. McLaughlin, and B. Mulgrew, “Complex-valued radial basis function network, Part I: Network architecture and learning algorithms,” Signal
Processing, vol. 35, no. 1, pp. 19–31, Jan. 1994.
2. S. Chen, S. McLaughlin, and B. Mulgrew, “Complex-valued radial basis function
network, Part II: Application to digital communications channel equalisation,”
Signal Processing, vol. 36, no. 2, pp. 175–188, March 1994.
3. A. Uncini, L. Vecci, P. Campolucci, and F. Piazza, “Complex valued neural
networks with adaptive spline activation function for digital radio links nonlinear
equalization,” IEEE Trans. Signal Processing, vol. 47, no. 2, pp. 505–514, Feb.
1999.
4. T. Kim and T. Adali, “Approximation by fully complex multilayer perceptrons,”
Neural Computation, vol. 15, no. 7, pp. 1641–1666, July 2003.
5. C.-C. Yang and N. K. Bose, “Landmine detection and classiﬁcation with complexvalued hybrid neural network using scattering parameters dataset,” IEEE Trans.
Neural Networks, vol. 16, no. 3, pp. 743–753, May 2005.
6. M. B. Li, G. B. Guang, P. Saratchandran, and N. Sundararajan, “Fully complex
extreme learning machine,” Neurocomputing, vol. 68, pp. 306–314, Oct. 2005.
7. A. Hirose, Complex Valued Neural Networks. Berlin: Springer-Verlag, 2006.
8. S. Chen, X. Hong, C. J. Harris, and L. Hanzo, “Fully complex-valued radial
basis function networks: Orthogonal least squares regression and classiﬁcation,”
Neurocomputing, vol. 71, nos. 16-18, pp. 3421–3433, Oct. 2008.
9. T. Nitta, Ed., Complex-Valued Neural Networks: Utilizing High-Dimensional
Parameters. New York: Information Science Reference, 2009.
10. A. S. Gangal, P. K. Kalra, and D. S. Chauhan, “Inversion of complex valued
neural networks using complex back-propagation algorithm,” Int. J. Mathematics and Computers in Simulation, vol. 3, no. 1, pp. 1–8, 2009.
11. M. Kobayashi, “Exceptional reducibility of complex-valued neural networks,”
IEEE Trans. Neural Networks, vol. 21, no. 7, pp. 1060–1072, July 2010.
12. S. A. Billings, “Identiﬁcation of nonlinear systems – a survey,” IEE Proc. D,
vol. 127, no. 6, pp. 272–285, Nov. 1980.
13. E. W. Bai, “An optimal two-stage identiﬁcation algorithm for HammersteinWiener nonlinear systems,” Automatica, vol. 34, no. 3, pp. 333–338, March
1998.
14. Y. Zhu, “Estimation of an N-L-N Hammerstein-Wiener model,” Automatica,
vol. 38, no. 9, pp. 1607–1614, Sept. 2002.
15. J. Schoukens, J. G. Nemeth, P. Crama, Y. Rolain, and R. Pintelon, “Fast
approximate identiﬁcation of nonlinear systems,” Automatica, vol. 39, no. 7,
pp. 1267–1274, July 2003.
16. K. Hsu, T. Vincent, and K. Poolla, “A kernel based approach to structured
nonlinear system identiﬁcation part I: algorithms,” in Proc. 14th IFAC Symp.
System Identi�cation (Newcastle, Australia), March 29-31, 2006, 6 pages.
17. K. Hsu, T. Vincent, and K. Poolla, “A kernel based approach to structured
nonlinear system identiﬁcation part II: convergence and consistency,” in Proc.
14th IFAC Symp. System Identi�cation (Newcastle, Australia), March 29-31,
2006, 6 pages.
18. I. W. Hunter and M. J. Korenberg, “The identiﬁcation of nonlinear biological
systems: Wiener and Hammerstein cascade models,” Biological Cybernetics,
vol. 55, nos. 2-3, pp. 135–144, 1986.
19. A. Kalafatis, N. Ariﬁn, L. Wang, and W. R. Cluett, “A new approach to the
identiﬁcation of pH processes based on the Wiener model,” Chemical Engineering Science, vol. 50, no. 23, pp. 3693–3701, Dec. 1995.
20. A. D. Kalafatis, L. Wang, and W. R. Cluett, “Identiﬁcation of Wiener-type
nonlinear systems in a noisy environment,” Int. J. Control, vol. 66, no. 7,
pp. 923–941, 1997.
21. Y. Zhu, “Distillation column identiﬁcation for control using Wiener model,” in
Proc. 1999 American Control Conference (San Diego, USA), June 2-4, 1999,
pp. 3462–3466.
22. J. C. Gomez, A. Jutan, and E. Baeyens, “Wiener model identiﬁcation and
predictive control of a pH neutralisation process,” IEE Proc. Control Theory
and Applications, vol. 151, no. 3, pp. 329–338, May 2004.
23. A. Hagenblad, L. Ljung, and A. Wills, “Maximum likelihood identiﬁcation of
Wiener models,” Automatica, vol. 44, no. 11, pp. 2697–2705, Nov. 2008.
24. W. Greblicki, “Nonparametric identiﬁcation of Wiener systems,” IEEE Trans.
Information Theory, vol. 38, no. 5, pp. 1487–1493, Sept. 1992.
25. I. Skrjanc, S. Blazic, and O. E. Agamennoni, “Interval fuzzy modeling applied
to Wiener models with uncertainties,” IEEE Trans. Systems, Man and Cybernetics, Part B, vol. 35, no. 5, pp. 1092–1095, Oct. 2005.
26. G. Farin, Curves and Surfaces for Computer-Aided Geometric Design: A Practical Guide. Fourth Edition. Boston: Academic Press, 1996.
27. C. De Boor, A Practical Guide to Splines. New York: Spring Verlag, 1978.
28. T. Kavli, “ASMOD – an algorithm for adaptive spline modelling of observation
data,” Int. J. Control, vol. 58, no. 4, pp. 947–967, 1993.
29. M. Brown and C. J. Harris, Neurofuzzy Adaptive Modelling and Control. Hemel
Hempstead: Prentice Hall, 1994.
30. C. J. Harris, X. Hong, and Q. Gan, Adaptive Modelling, Estimation and Fusion
from Data: A Neurofuzzy Approach. Berlin: Springer-Verlag, 2002.
31. Y. Yang, L. Guo, and H. Wang, “Adaptive statistic tracking control based on
two-step neural networks with time delays,” IEEE Trans. Neural Networks,
vol. 20, no. 3, pp. 420–429, March 2009.
32. C. J. Clark, G. Chrisikos, M. S. Muha, A. A. Moulthrop, and C. P. Silva, “Timedomain envelope measurement technique with application to wideband power
ampliﬁer modeling,” IEEE Trans. Microwave Theory and Techniques, vol. 46,
no. 12, pp. 2531–2540, Dec. 1998.
33. J. H. K. Vuolevi, T. Rahkonen, and J. P. A. Manninen, “Measurement technique for characterizing memory eﬀects in RF power ampliﬁers,” IEEE Trans.
Microwave Theory and Techniques, vol. 49, no. 8, pp. 1383–1389, Aug. 2001.
34. C.-H. Lin, H.-H. Chen, Y.-Y. Wang, and J.-T. Chen, “Dynamically optimum
lookup-table spacing for power ampliﬁer predistortion linearization,” IEEE Trans.
Microwave Theory and Techniques, vol. 54, no. 5, pp. 2118–2127, May 2006.
35. B. Ai, Z.-Y. Yang, C.-P. Pan, S.-G. Tang, and T. T. Zhang, “Analysis on LUT
based predistortion method for HPA with memory,” IEEE Trans. Broadcasting,
vol. 53, no. 1, pp. 127–131, March 2007.
36. L. Ding, G. T. Zhou, D. R. Morgan, Z. Ma, J. S. Kenney, J. Kim, and C. R. Giardina, “A robust digital baseband predistorter constructed using memory polynomials,” IEEE Trans. Communications, vol. 52, no. 1, pp. 159–165, Jan. 2004.
37. D. Zhou and V. E. DeBrunner, “Novel adaptive nonlinear predistorters based
on the direct learning algorithm,” IEEE Trans. Signal Processing, vol. 55, no. 1,
pp. 120–133, Jan. 2007.
38. V. P. G. Jim´enez, Y. Jabrane, A. G. Armada, and B. Ait Es Said, “High power
ampliﬁer pre-distorter based on neural-fuzzy systems for OFDM signals,” IEEE
Trans. Broadcasting, vol. 57, no. 1, pp. 149–158, March 2011.
39. S. Chen, “An eﬃcient predistorter design for compensating nonlinear memory
high power ampliﬁer,” IEEE Trans. Broadcasting, vol. 57, no. 4, pp. 856–865,
Dec. 2011.
40. X. Hong and S. Chen, “Modeling of complex-valued Wiener systems using Bspline neural network,” IEEE Trans. Neural Networks, vol. 22, no. 5, pp. 818–
825, May 2011.
41. B. Igelnik, “Kolmogorov’s spline complex network and adaptive dynamic modeling of data,” in: T. Nitta, Ed., Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters. New York: Information Science Reference:
2009, pp. 56–78.
42. M. Scarpiniti, D. Vigliano, R. Parisi, and A. Unicinis, “Flexible blind signal
separation in the complex domain,” in: T. Nitta, Ed., Complex-Valued Neural
Networks: Utilizing High-Dimensional Parameters. New York: Information
Science Reference, 2009, pp. 284–323.
43. A. A. M. Saleh, “Frequency-independent and frequency-dependent nonlinear
models of TWT ampliﬁers,” IEEE Trans. Communications, vol. COM-29,
no. 11, pp. 1715–1720, Nov. 1981.
44. M. Honkanen and S.-G. H¨aggman, “New aspects on nonlinear power ampliﬁer
modeling in radio communication system simulations,” in Proc. PIMRC'97
(Helsinki, Finland), Sept. 1-4, 1997, pp. 844–848.
45. P. M. Grant, S. McLaughlin, H. Aghvami, and S. Fletcher, “Green radio –
towards sustainable wireless networks,” Mobile VCE Core 5 Programme Presentation, April 2009. Available on-line from
http://www.ee.princeton.edu/seminars/iss/Spring2009/slides/grant.pdf
46. L. Hanzo, S. X. Ng, T. Keller, and W. Webb, Quadrature Amplitude Modulation:
From Basics to Adaptive Trellis-Coded, Turbo-Equalised and Space-Time Coded
OFDM, CDMA and MC-CDMA Systems. Chichester, UK: John Wiley, 2004.
47. L. Hanzo, M. M¨unster, B. J. Choi, and T. Keller, OFDM and MC-CDMA for
Broadband Multi-User Communications, WLANs and Broadcasting. Chichester,
UK: John Wiley, 2003.