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Titlebook: Neural Nets WIRN VIETRI-96; Proceedings of the 8 Maria Marinaro,Roberto Tagliaferri Conference proceedings 1997 Springer-Verlag London Limi

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樓主: Sparkle
41#
發(fā)表于 2025-3-28 17:53:27 | 只看該作者
Neural Nets WIRN VIETRI-96978-1-4471-0951-8Series ISSN 1431-6854
42#
發(fā)表于 2025-3-28 20:21:48 | 只看該作者
43#
發(fā)表于 2025-3-28 23:25:07 | 只看該作者
44#
發(fā)表于 2025-3-29 05:10:07 | 只看該作者
45#
發(fā)表于 2025-3-29 10:57:02 | 只看該作者
A New Incremental Learning TechniqueWe present a new type of constructive algorithm for incremental learning. The algorithm overcomes many of the problems associated with standard back propagation such as speed and optimum network size. We investigate the ability of the network to learn and test the resulting generalisation of the network.
46#
發(fā)表于 2025-3-29 13:24:14 | 只看該作者
Proof of the Universal Approximation of a Set of Fuzzy functionsIn the last few years neuro-fuzzy systems have been investigated by many researchers [2,3]..The possibility of combine linguistics and numerical knowledges make those models very attractive.
47#
發(fā)表于 2025-3-29 16:39:03 | 只看該作者
Classification of Structures by Recurrent Neural NetworksWe introduce neural networks for the classification of structures, LRAAM-based networks, Cascade-Correlation Networks, and Neural Trees for structures are presented. We show how to compute the gradient for these networks. Moreover, we report some results on the computational power of these models.
48#
發(fā)表于 2025-3-29 21:27:12 | 只看該作者
49#
發(fā)表于 2025-3-30 00:33:17 | 只看該作者
https://doi.org/10.1007/978-1-4471-0951-8algorithms; cognition; fuzzy system; neural networks; pattern; pattern recognition; virtual reality; comple
50#
發(fā)表于 2025-3-30 06:42:01 | 只看該作者
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