Abstract
A rigorous mathematical analysis is presented of an adaline-like network operating in continuous time with spatially continuous inputs and outputs. Weights adapt continually, whether or not a training signal is present. It is shown that consistent input-output pairs can be learned perfectly provided every pattern is repeated at least once in every N successive inputs, and the input patterns are nearly orthogonal, depending on N.
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.
