盟军
发表于 2025-3-25 04:35:36
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
Occupation
发表于 2025-3-25 10:49:54
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外貌
发表于 2025-3-25 14:38:48
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
新手
发表于 2025-3-25 16:28:33
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
bourgeois
发表于 2025-3-25 20:48:34
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
HERTZ
发表于 2025-3-26 03:33:57
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/186358.jpg
Aggregate
发表于 2025-3-26 07:44:31
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SLUMP
发表于 2025-3-26 08:34:21
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肉身
发表于 2025-3-26 16:34:42
Bio-Inspired Systems: Computational and Ambient Intelligence978-3-642-02478-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
Cerebrovascular
发表于 2025-3-26 17:32:21
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.