核回归在HRBF网络表面重构中的应用

2008 IEEE International Workshop on Haptic Audio visual Environments and Games Pub Date : 2008-11-21 DOI:10.1109/HAVE.2008.4685317

F. Bellocchio, N. A. Borghese, S. Ferrari, Vincenzo Piuri

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引用次数: 6

摘要

分层径向基函数(HRBF)网络是一种适用于曲面重建问题的神经网络模型。它的非迭代配置算法需要对网络单元中心的表面进行估计。在本文中，我们从准确率、所需单元和计算时间方面分析了不同估计器在训练HRBF网络中的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Kernel regression in HRBF networks for surface reconstruction

The Hierarchical Radial Basis Function (HRBF) Network is a neural model that proved its suitability in the surface reconstruction problem. Its non-iterative configuration algorithm requires an estimate of the surface in the centers of the units of the network. In this paper, we analyze the effect of different estimators in training HRBF networks, in terms of accuracy, required units, and computational time.

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2008 IEEE International Workshop on Haptic Audio visual Environments and Games

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