A learning approximator for compact representation of experimental mappings

Riccardo Carotenuto

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A learning approximator for compact representation of experimental mappings

机译：学习逼近器，用于实验映射的紧凑表示

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摘要

An iterative technique is proposed to efficiently represent n-dimensional discrete mappings from experimental sampled data. The original sampled mapping is approximated, under some assumptions, with a proper set of one-dimensional arrays. The proposed technique highly reduces the severe memory requirements of classical memory-based techniques. A convergence discussion on the proposed algorithm and application examples are presented.

机译：提出了一种迭代技术，可以有效地从实验采样数据表示n维离散映射。在某些假设下，使用一组适当的一维数组对原始采样映射进行了近似。所提出的技术极大地降低了基于经典内存技术的严格内存需求。提出了关于所提出算法的收敛性讨论和应用实例。

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来源
《International Journal of Adaptive Control and Signal Processing》 |2003年第5期|p.353-361|共9页
作者
Riccardo Carotenuto;
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作者单位

D.I.M.E.T., Universita degli Studi "Mediterranea" di Reggio Calabria, Loc. Feo de Vito, 89060-Reggio Calabria, Italy;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
原文格式 PDF
正文语种 eng
中图分类电工技术;
关键词
multidimensional mapping representation; iterative approximation;

机译：多维映射表示;迭代逼近;

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A learning approximator for compact representation of experimental mappings

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