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Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I David W. Pearson,Nigel C. Steele,Rudolf F. Albrech Conference proceedin

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樓主: 愚蠢地活
11#
發(fā)表于 2025-3-23 09:52:12 | 只看該作者
Bringing Gender Issues to Technology Design real traffic data where possible, and produces a network amongst twenty given nodes with two-shortest-node-disjoint routing between node pairs. Both routes are fully resourced, guaranteeing the network will survive a single component failure. The results are compared with the earlier ‘hand-crafted’
12#
發(fā)表于 2025-3-23 17:50:16 | 只看該作者
13#
發(fā)表于 2025-3-23 19:46:37 | 只看該作者
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發(fā)表于 2025-3-24 00:38:54 | 只看該作者
Technological Change and Cultural Ceilingssatisfy a large number of constraints and use as few distinct frequencies as possible. This problem is known to be NP-complete. We describe the application of a genetic algorithm to the solution of the RLFAP. The standard crossover operators were found to be of limited use due to the highly epistati
15#
發(fā)表于 2025-3-24 04:28:09 | 只看該作者
https://doi.org/10.1007/978-3-322-94954-7of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing distributed feedforward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hard
16#
發(fā)表于 2025-3-24 10:34:56 | 只看該作者
17#
發(fā)表于 2025-3-24 14:06:10 | 只看該作者
Community Engagement in Higher Educationsensory-motor agent as a collection of activity schemata. The activity schema permits a useful distribulion of neurons in a conceptual space, creating concepts based on action and sensation. Such approach is inspired in the theory of the Swiss psychologist and epistemologist Jean Piaget, and intends
18#
發(fā)表于 2025-3-24 17:54:09 | 只看該作者
Lamea Elle Shaaban-Maga?a,Melanie L. MillerGaussian activation function and metrics induced by an inner product. The description of functional equivalent parameterizations is used for proposition of new genetic learning rules that operate only on a small part of the whole weight space.
19#
發(fā)表于 2025-3-24 20:25:24 | 只看該作者
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發(fā)表于 2025-3-25 02:29:29 | 只看該作者
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