The authors introduce a continuous stochastic generative model that can model continuous data, with a simple and reliable training algorithm. The architecture is a continuous restricted Boltzmann machine, with one step of Gibbs sampling, to minimise contrastive divergence, replacing a time-consuming relaxation search. With a small approximation, the training algorithm requires only addition and multiplication and is thus computationally inexpensive in both software and hardware. The capabilities of the model are demonstrated and explored with both artificial and real data.157

An adaptive stochastic classifier based on a simple, novel neural architecture -the Continuous Restricted Boltzmann Machine (CRBM) is demonstrated. Together with sensors and signal conditioning circuits, the classifier is capable of measuring and classifying (with high accuracy) the H + ion concentration, in the presence of both random noise and sensor drift. Training on-line, the stochastic classifier is able to overcome significant drift of real incomplete sensor data dynamically. As analogue hardware, this signal-level sensor fusion scheme is therefore suitable for real-time analysis in a miniaturised multisensor microsystem such as a Lab-in-a-Pill (LIAP).