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

This paper presents a neural approach to sensor modelling and classification as the basis of local data fusion in a wireless sensor network. Data distributions are non-Gaussian. Data clusters are sufficiently complex that the classification problem is markedly non-linear. We prove that a Continuous Restricted Boltzmann Machine can model complex data distributions and can autocalibrate against real sensor drift. To highlight the adaptation, two trained but subsequently non-adaptive neural classifiers (SLP and MLP) were employed as benchmarks.