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Titlebook: Advances in Neural Networks – ISNN 2018; 15th International S Tingwen Huang,Jiancheng Lv,Alexander V. Tuzikov Conference proceedings 2018 S

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51#
發(fā)表于 2025-3-30 10:18:02 | 只看該作者
52#
發(fā)表于 2025-3-30 14:29:15 | 只看該作者
Modeling Hysteresis Using Non-smooth Neural Networkshe parameters of the non-smooth neurons can be determined automatically based on the optimization of the cost function. Finally, the experimental results are illustrated to demonstrate the modeling performance of the proposed modeling method.
53#
發(fā)表于 2025-3-30 19:11:56 | 只看該作者
54#
發(fā)表于 2025-3-30 23:42:16 | 只看該作者
55#
發(fā)表于 2025-3-31 04:15:58 | 只看該作者
56#
發(fā)表于 2025-3-31 07:46:17 | 只看該作者
Alexander I. Popov,Jean-Marie Lehnh and stay bounded within a small neighborhood of ideal weights without a persistence of excitation condition. Finally, by resorting to the Lyapunov theory, the performance of the proposed kinetics identification method is analyzed.
57#
發(fā)表于 2025-3-31 10:20:33 | 只看該作者
58#
發(fā)表于 2025-3-31 15:14:47 | 只看該作者
Automatic Inference of Cross-Modal Connection Topologies for X-CNNss, gaining an additional 9% of accuracy. From further considerations, we conclude that the presented methodology takes less time than any manual approach would, whilst also significantly reducing the design complexity. The application of the methods is fully automated and implemented in Xsertion library (Code is publicly available at .).
59#
發(fā)表于 2025-3-31 20:55:56 | 只看該作者
60#
發(fā)表于 2025-3-31 22:32:36 | 只看該作者
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