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Titlebook: Bio-Inspired Systems: Computational and Ambient Intelligence; 10th International W Joan Cabestany,Francisco Sandoval,Juan M. Corchado Confe

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21#
發(fā)表于 2025-3-25 04:35:36 | 只看該作者
Der Psychosomatische Dialog als ?Putzrede“uitable in the system to cooperate with, as well as which tools are needed to achieve the common goal in the system in a cooperative way. On the other hand, awareness allows users to be aware of others’ activities each and every moment. Information about others’ activities combined with their intent
22#
發(fā)表于 2025-3-25 10:49:54 | 只看該作者
23#
發(fā)表于 2025-3-25 14:38:48 | 只看該作者
Die Katharsis im psychodramatischen Spiel,his problem for an artificial neural network trained to solve the XOR problem. The network is transformed into the equivalent . which provides a symbolic representation of the knowledge embedded in the network. We develop a mathematical model for the evolution of the fuzzy rule-base parameters durin
24#
發(fā)表于 2025-3-25 16:28:33 | 只看該作者
Sammelrezensionen Neues vom Psychodrama,t degrees correlation . The models for the neurons, synapses and plasticity rules (STDP) have a common biophysics basis. The neural network is simulated using a mixed analog-digital platform, which performs real-time simulations. We describe the study context, and the models for the neurons and for
25#
發(fā)表于 2025-3-25 20:48:34 | 只看該作者
https://doi.org/10.1007/978-3-322-95898-3 data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training
26#
發(fā)表于 2025-3-26 03:33:57 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/186358.jpg
27#
發(fā)表于 2025-3-26 07:44:31 | 只看該作者
28#
發(fā)表于 2025-3-26 08:34:21 | 只看該作者
29#
發(fā)表于 2025-3-26 16:34:42 | 只看該作者
Bio-Inspired Systems: Computational and Ambient Intelligence978-3-642-02478-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
30#
發(fā)表于 2025-3-26 17:32:21 | 只看該作者
https://doi.org/10.1007/978-3-322-95898-3 data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training under specific conditions.
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