Abstract
A new family of stochastic gradient adaptive filter algorithms is proposed which is based on mixed error norms. These algorithms combine the advantages of different error norms, for example the conventional, relatively well-behaved, least mean square algorithm and the more sensitive, but better converging, least mean fourth algorithm. A mixing parameter is included which controls the proportions of the error norms and offers an extra degree of freedom within the adaptation. A system identification simulation is used to demonstrate the performance of a least mean mixed-norm (square and fourth) algorithm.
Sign-in/Register to access full text options 
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.
