Abstract
Reproducing Hilbert space (RKHS)-based adaptive algorithms have attracted increased attention in machine learning and nonlinear signal processing with applications in visible light communications, radar, radio frequency communications and others. However, performance of RKHS-based algorithms is highly sensitive to a suitable learning criterion. In this regard, the Versoria criterion-based adaptive filtering has gained interest in recent works due to its superior convergence characteristics as compared to the popular criterion such as minimum mean square error, and maximum correntropy criterion. Therefore, in this brief, a novel random Fourier feature (RFF)-based kernel recursive maximum Versoria criterion (KRMVC) algorithm is proposed. Convergence analysis is presented next for the proposed RFF-KRMVC algorithm as guarantees of the promised performance benefits. Lastly, the analytical results are validated by corresponding computer-simulations over practical application-scenarios considered in this brief.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE Transactions on Circuits and Systems II: Express Briefs
Disclaimer: 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.