察觉
发表于 2025-3-30 11:01:57
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顾客
发表于 2025-3-30 16:08:09
David Casassas,Daniel Raventós,Julie Warkhe experiments performed on a . benchmark dataset show that the adaptive nature of the proposed neural network model enables the compensation of the errors caused by inexact calculations by simultaneously increasing its performance and reducing power consumption.
Brain-Imaging
发表于 2025-3-30 18:29:00
0302-9743 0. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.978-3-642-20281-0978-3-642-20282-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
战胜
发表于 2025-3-31 00:21:26
Roberto Merrill,Catarina Neves,Bru Laínne learning called Inductive Logic Programming (ILP). The feature of predicate invention in ILP is particularly relevant. Examples of actually discovered abstract concepts in experiments are described.
AXIS
发表于 2025-3-31 01:14:12
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harmony
发表于 2025-3-31 07:51:08
Exploring the Basic Income Guaranteens is initially calculated. To constitute the approximation basis only those functions are taken which ensure the fastest error decrease down to a desired level. Working examples are presented, demonstrating a very good generalization property of this method.
LARK
发表于 2025-3-31 12:06:54
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滴注
发表于 2025-3-31 13:51:38
Roberto Merrill,Catarina Neves,Bru Laínlculated on-line and used for prediction. Thanks to it, the control policy is calculated from a quadratic programming problem. Good control accuracy and computational efficiency of the discussed algorithm are shown in the control system of a chemical reactor for which the classical MPC strategy based on a linear model is unstable.
fleeting
发表于 2025-3-31 18:24:44
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蚊帐
发表于 2025-3-31 21:57:06
Basic Income Guarantee and Politicsusing four datasets with diverse amounts of patterns and with different neural network architectures. All results show a significant increase in computation speed, which is reduced nearly linear with the number of cores for problems with very large training datasets.