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

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Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298, 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. 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

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Item Type	Book or Report Section
URI	https://reading-clone.eprints-hosting.org/id/eprint/34104
Item Type	Book or Report Section
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
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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Date Deposited:	07 Oct 2013 08:36	Date item deposited into CentAUR
Last Modified:	09 Jul 2021 09:24	Date item last modified

References

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