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Complex-valued b-spline neural networks for modelling and inverse of wiener systems

Hong, X., Chen, S. and Harris, C. J. (2013) Complex-valued b-spline neural networks for modelling and inverse of wiener systems. In: Hirose, A. (ed.) Complex-Valued Neural Networks: Advances and Applications. John Wiley & Sons, Hoboken, NJ, pp. 209-233.

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To link to this item DOI: 10.1002/9781118590072.ch9

Abstract/Summary

Communication signal processing applications often involve complex-valued (CV) functional representations for signals and systems. CV artificial neural networks have been studied theoretically and applied widely in nonlinear signal and data processing [1–11]. Note that most artificial neural networks cannot be automatically extended from the real-valued (RV) domain to the CV domain because the resulting model would in general violate Cauchy-Riemann conditions, and this means that the training algorithms become unusable. A number of analytic functions were introduced for the fully CV multilayer perceptrons (MLP) [4]. A fully CV radial basis function (RBF) nework was introduced in [8] for regression and classification applications. Alternatively, the problem can be avoided by using two RV artificial neural networks, one processing the real part and the other processing the imaginary part of the CV signal/system. A even more challenging problem is the inverse of a CV

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:34104
Uncontrolled Keywords:complex-valued wiener systems; wiener system identification; wiener system inverse; high-power amplifier model; digital predistorter design
Publisher:John Wiley & Sons

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