Detection and Prognostics on Low Dimensional Systems

The methods rely on estimating the conditional probability distribution of the output of the system at a future time given knowledge of the current state of the system. We show how to estimate these conditional probabilities using a variety of techniques, including bagged neural networks and kernel methods such as Gaussian Process Regression (GPR).

The results are compared with standard method such as the nearest neighbor algorithm. We demonstrate the algorithms on a real-world data set and a simulated data set. The real-world data set consists of the intensity of an NH3 laser.