Abstract
A probabilistic, “neural” approach to sensor modelling and classification is described, performing local data fusion in a wireless system for embedded sensors using a continuous restricted Boltzmann machine (CRBM). The sensor data clusters are non-Gaussian and their classification is non-linear. A CRBM is shown to be able to model complex data distributions and to adjust autonomously to measured sensor drift. Performance is compared with that of single layer and multilayer neural classifiers. It is shown that a CRBM can resolve the problem of catastrophic interference that is typical of associative memory based models.
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.
