A hybrid training method for B-spline neural networks

Current and past research has brought up new views related to the optimization of neural networks. For a fixed structure, second order methods are seen as the most promising. From previous works we have shown how second order methods are of easy applicability to a neural network. Namely, we have proved how the Levenberg-Marquard possesses not only better convergence but how it can assure the convergence to a local minima. However, as any gradient-based method, the results obtained depend on the startup point. In this work, a reformulated Evolutionary algorithm - the Bacterial Programming for Levenberg-Marquardt is proposed, as an heuristic which can be used to determine the most suitable starting points, therefore achieving, in most cases, the global optimum.

Monitoring of urban transportation networks using wireless sensor networks

Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Data storage system for wireless sensor networks

Dissertação de mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015