The combination of neural estimates in prediction and decision problems

In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.

Hovercraft telemanipulation virtual models and interface design

A control system was designed to allow humans to manually drive an, usually automatic, two wheeled hovercraft. The size, the mass and the way of driving this vehicle proves to be an issue for the everyday, untrained person to achieve. During this thesis several control layouts were designed with the objective of creating an intuitive and easy way of driving such a vehicle. At the end two where usertested using a simulation (also developed during this thesis) of the said hovercraft set against obstacles similar to those expected to be encountered on its real environment. The two layouts are just slightly apart in performance but numerous issues were found that can be used to redesign a better control layout. This means that no definitive winner was found but a foundation for a better design was indeed found.