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Titlebook: Artificial Intelligence Applications and Innovations; 8th IFIP WG 12.5 Int Lazaros Iliadis,Ilias Maglogiannis,Harris Papadopo Conference pr

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樓主: DIGN
11#
發(fā)表于 2025-3-23 12:48:48 | 只看該作者
Fine Tuning of a Wet Clutch Engagement by Means of a Genetic Algorithm. Then by knowing the system response of the test bench in the frequency domain, GA will be used again to fine tuning this parameterized signal. The result is then compared to those performances of using signal without fine tuning step. It is shown that after applying the fine tuning method, the resulted signal can achieve a better performance.
12#
發(fā)表于 2025-3-23 16:58:57 | 只看該作者
On the Design and Training of Bots to Play Backgammon Variantsesigned, trained and evaluated for each one of these variants. This paper presents the details of the architecture and the training procedure for each case. New expert features as inputs to the networks are also introduced, whereas experimental results demonstrate improvement over previous versions of ..
13#
發(fā)表于 2025-3-23 18:05:40 | 只看該作者
14#
發(fā)表于 2025-3-24 01:43:02 | 只看該作者
15#
發(fā)表于 2025-3-24 03:18:50 | 只看該作者
16#
發(fā)表于 2025-3-24 08:56:16 | 只看該作者
Walter F. L. Bogaerts,Marc J. S. Vancoille was to develop a Multi-Layer Perceptron neural network model which considered significant variables from an a priori developed robust regression model. The application of robust regression could be considered in selecting the input variables in a neural network model.
17#
發(fā)表于 2025-3-24 13:15:16 | 只看該作者
18#
發(fā)表于 2025-3-24 17:36:52 | 只看該作者
Sang-Yun Han,Tschangho John Kimrepresentable with these networks (in contrast to classical ANNs) and that makes them easily learnable from training datasets according to a developed method of ANN architecture optimization. Methodology for comparing quality of different representations is illustrated by applying developed method in time series prediction and robot control.
19#
發(fā)表于 2025-3-24 19:22:16 | 只看該作者
Kathi Kellenberger,Clayton Groomage classification rates. From this fully distributed and privacy preserving mutual validation a coarse-grained matrix can be formed to map all members. We demonstrate that it is possible to fully exploit this mutual validation matrix to efficiently train another regularization network as a meta learner combiner for the committee.
20#
發(fā)表于 2025-3-25 00:39:49 | 只看該作者
Applications with asyncio and Twistedspace approach for achieving its scene categorizations. The key element is a physics engine that is used both for the construction of information-rich physical features and for the prediction of how a given situation might evolve.
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