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Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights. In this paper, the behavior of a recently proposed variation of this algorithm(More)
The temporal character of the input is, generally, not taken into account in the neural models. This paper presents an extension of the FasArt model focused on the treatment of temporal signals. FasArt model is proposed as an integration of the characteristic elements of the Fuzzy System Theory in an ART architecture. A duality between the activation(More)
The arriving order of data is one of the intrinsic properties of a signal. Therefore, techniques dealing with this temporal relation are required for identification and signal processing tasks. To perform a classification of the signal according with its temporal characteristics, it would be useful to find a feature vector in which the temporal attributes(More)
In this letter, an improvement of the recently developed neighborhood-based Levenberg-Marquardt (NBLM) algorithm is proposed and tested for neural network (NN) training. The algorithm is modified by allowing local adaptation of a different learning coefficient for each neighborhood. This simple add-in to the NBLM training method significantly increases the(More)