Neural Networks in Signal Processing

tering, system of extended and updated versions of papers that have originally been prese the 2003 IEEE International Workshop on Neural Networks for Signal Pro (NNSP03) in Toulouse, France (September 17–19, 2003) (now called Works Machine Learning for Signal Processing—MLSP). The authors have been inv contribute to this special issue on the basis of originality, technical qualit relevance of the papers presented at the workshop. The invited papers hav subjected to the usual rigorous peer review process by anonymous reviewer editors of this special issue are convinced that this selection of excellent provides the reader with an up-to-date account of what neural networks h offer for today’s challenging signal processing problems. The papers can be grouped into the following categories: (1) clus classification & regression, (2) adaptive filtering, noise estimation & identification, (3) recursive learning, and (4) blind inverse problem solving.

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