We are focusing on three alternative techniques that can be used to empirically select predictors for failure prediction purposes. The selected techniques have all different assumptions about the… Expand

We are focusing on three alternative techniques-linear discriminant analysis, logit analysis and genetic algorithms-that can be used to empirically select predictors for neural networks in failure prediction.Expand

We show how a single framework can be used for both the specification of large systems, the modular decomposition of the system into smaller units and the refinement of the modules into program modules that can be described in a standard programming language.Expand

Neural networks and machine learning methods have proved in many ways and in a number of publications to be real challengers to statistical methods - especially to logit and discriminant analysis - in predicting failures.Expand

Superposition refinement enhances an algorithm by superposing one computation mechanism onto another mechanism, in a way that preserves the behavior of the original mechanism.Expand

We generalize the definition of differential action, allowing the use of arbitrary relations over model variables and their time derivatives in modelling continuous-time dynamics.Expand