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2006

Adaptive Sensor Modelling and Classification using a Continuous Restricted Boltzmann Machine (CRBM)

12 years 3 months ago
Adaptive Sensor Modelling and Classification using a Continuous Restricted Boltzmann Machine (CRBM)
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. r 2007 Elsevier B.V. All rights reserved.
Tong Boon Tang, Alan F. Murray
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Where ESANN
Authors Tong Boon Tang, Alan F. Murray
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