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Titlebook: Computational Intelligence and Bioinspired Systems; 8th International Wo Joan Cabestany,Alberto Prieto,Francisco Sandoval Conference procee

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樓主: decoction
41#
發(fā)表于 2025-3-28 17:57:56 | 只看該作者
42#
發(fā)表于 2025-3-28 22:09:19 | 只看該作者
43#
發(fā)表于 2025-3-28 23:56:59 | 只看該作者
Memetic Algorithms to Product-Unit Neural Networks for Regressionry similar individuals, we optimize just a few different individuals. This approach is less computationally expensive. Our results show a very interesting performance when this model is compared to other standard algorithms. The proposed model is evaluated in the optimization of the structure and weights of product-unit based neural networks.
44#
發(fā)表于 2025-3-29 04:09:13 | 只看該作者
Artificial Neural Networks Based on Brain Circuits Behaviour and Genetic Algorithmsis shown in feed-forward multilayer artificial neural networks, specifically created to solve a simple problem. We also illustrate the benefits obtained with these new nets from a comparison with previous results achieved by the optimal Artificial Neural Networks used so far for solving the same problem.
45#
發(fā)表于 2025-3-29 09:51:11 | 只看該作者
The After-Hyperpolarization Amplitude and the Rise Time Constant of IPSC Affect the Synchronization g to the emergence of synchronous regimes in a network of FS interneurons coupled by chemical and electrical synapses. We also compare our results with those recently obtained for a pair of coupled Integrate & Fire neural models [1].
46#
發(fā)表于 2025-3-29 14:06:10 | 只看該作者
47#
發(fā)表于 2025-3-29 15:50:40 | 只看該作者
Weitere Storytelling-Methoden und Varianten,he order of convergence is not related to the complexity of the probabilistic model, and that an algorithm whose probabilistic model mimics the structure of the objective function does not guarantee a low order of convergence.
48#
發(fā)表于 2025-3-29 20:00:26 | 只看該作者
49#
發(fā)表于 2025-3-30 02:22:33 | 只看該作者
50#
發(fā)表于 2025-3-30 06:45:59 | 只看該作者
Role of Function Complexity and Network Size in the Generalization Ability of Feedforward Networks hidden layer of neurons further improves the generalization ability for very complex functions. Quasi-random generated Boolean functions were also analyzed and we found that in this case the generalization ability shows small variability across different network sizes both with one and two hidden layer network architectures.
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