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Titlebook: Neural Networks; An Introduction Berndt Müller,Joachim Reinhardt,Michael T. Strickl Book 1995Latest edition Springer-Verlag Berlin Heidelbe

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41#
發(fā)表于 2025-3-28 17:18:24 | 只看該作者
Coupled Neural Networksns. On the other hand, any general “recurrent” neural network can be considered to be represente by a feed-forward perceptron, albeit one with possibly very many layers. The reason for this strange equivalence is that the temporal evolution (3.5) of an arbitrary network constructed from binary neuro
42#
發(fā)表于 2025-3-28 19:16:50 | 只看該作者
43#
發(fā)表于 2025-3-28 23:41:26 | 只看該作者
44#
發(fā)表于 2025-3-29 06:39:02 | 只看該作者
45#
發(fā)表于 2025-3-29 09:08:20 | 只看該作者
46#
發(fā)表于 2025-3-29 13:21:33 | 只看該作者
Neural Networks978-3-642-57760-4Series ISSN 0939-3145
47#
發(fā)表于 2025-3-29 16:46:27 | 只看該作者
Associative Memoryutation on a neural network. An information storage device is called an ., if it permits the recall of information on the basis of a partial knowledge of its content, but without knowing its storage location. One also speaks of . memory.
48#
發(fā)表于 2025-3-29 23:22:12 | 只看該作者
49#
發(fā)表于 2025-3-30 00:58:47 | 只看該作者
VLSI and Neural Networksit it for practical and perhaps beneficial purposes, one must face the challenge of performing real-life neural computations. Here several approaches exist and have been widely explored. We enumerate the most important alternatives:
50#
發(fā)表于 2025-3-30 05:38:42 | 只看該作者
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