Abstract
A complex-valued gradient based algorithm for training nonlinear complex-valued finite impulse response (FIR) filters is derived. The proposed complex error-adaptive normalised nonlinear gradient descent (CEANNGD) and smoothed CEANNGD (SCEANNGD) algorithms are an improvement on the complex nonlinear gradient descent (CNGD) and the complex normalised nonlinear gradient descent (CNNGD) algorithm by including an adaptive term in the normalised learning rate of the CNNGD. This is achieved by performing a minimisation of the complex-valued instantaneous output error that has been approximated via a Taylor series expansion, which makes it suitable for the processing of nonlinear and nonstationary signals. Experiments on complex-valued coloured and nonlinear signals show that the CEANNGD and SCEANNGD algorithms outperform the standard CNNGD and CNGD algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
