A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Strong erbium luminescence in the near-infrared telecommunication window

A new type of near-infrared emitting rare-earth complex has been synthesised, consisting of three bis(perfluoroalkylsulfonyl)imide ligands and one 1,10-phenanthroline molecule. The chelate rings formed by the rare-earth ion and the bidentate ligands do not contain any carbon atoms and can hence be considered as 'inorganic' chelate rings. The absence of C-H stretching vibration modes in the first coordination sphere of the rare-earth ion and the presence of a light-harvesting moiety (1,10-phenanthroline) bound to the rare-earth ion result in a complex that can be efficiently excited and exhibits intense near-infrared luminescence. (C) 2004 Elsevier B.V. All rights reserved.