Abstract
The recently introduced augmented complex nonlinear gradient descent (ACNGD) algorithm for complex domain adaptive filtering which utilises the full second order statistical information is shown to be suitable for the processing of both circular and noncircular signals. By virtue of the underlying widely nonlinear model, the complex nonlinear gradient descent (CNGD) is shown to successfully model conventional system, however, unable to model the widely nonlinear system, and the ACNGD is capable to model both the conventional and widely nonlinear system. Simulations in adaptive modelling context for signals with different probability distributions and degrees of circularity support the analysis.
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Amadi, C.C., Ujang, B.C., Hashim, F.B. (2016). Nonlinear System Modelling Utilizing Second Order Augmented Statistics Complex Value Algorithm. In: Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture Notes in Electrical Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-32213-1_20
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DOI: https://doi.org/10.1007/978-3-319-32213-1_20
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