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- W1963907123 abstract "In this paper, a novel model of Morphological Neural Networks (MNN) is presented. MNNs use neurons with dendritic structure that bear a more faithful resemblance to the biological neurons of the celebral cortex than those used in various artificial neural networks. MNNs have the high capability of resolving some difficult non-linear problems and are getting more and more applicable. In this work we develop a new model of MNNs that are more powerful since each dendrite can work on a different orthonormal basis than the other dendrites in order to optimize the performance of the Orthonormal Basis Morphological Neural Network (OBMNN). OBMNNs compress automatically their size and show significantly better learning capabilities than the regular MNNs, which are a sub-group of OBMNNs. Validation experimental results are presented that demonstrate superior learning performance of OBMNNs over regular MNNs." @default.
- W1963907123 created "2016-06-24" @default.
- W1963907123 creator A5082617781 @default.
- W1963907123 date "2006-03-10" @default.
- W1963907123 modified "2023-09-26" @default.
- W1963907123 title "Morphological NNs with orthonormal basis dendrites" @default.
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- W1963907123 doi "https://doi.org/10.1145/1185448.1185503" @default.
- W1963907123 hasPublicationYear "2006" @default.
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