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Adaptive tiled Neural Networks

机译:自适应平铺神经网络

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摘要

In this paper, a novel function approximation approach based on a combination of conventional Neural Networks and tile coding approximators is proposed. The proposed approach can maintain the desired features of both approaches whiles eliminates the deficiencies of each method. The combination will reduce the sharpness of tile coding. It will also provide an easy way to adjust the accuracy/complexity of the approximation according to the function being approximated (adaptive tiling) and the subspace used on. In this algorithm, it is possible to construct the approximator with specified and various approximation accuracies in different subspaces. This feature enables us to allocate an arbitrary accuracy/complexity wherever a more accurate approximation is needed. Finally simulation studies are presented to show the efficiency of and applicability of the proposed approach.
机译:在本文中,提出了一种基于传统神经网络和瓦片编码近似器的新颖函数近似方法。所提出的方法可以保持两种方法的期望特征,同时消除了每种方法的缺陷。该组合将降低图块编码的清晰度。它还将提供一种简单的方法来根据近似函数(自适应平铺)和所使用的子空间来调整近似的精度/复杂度。在该算法中,可以在不同的子空间中构造具有指定和各种近似精度的近似器。此功能使我们能够在需要更精确的近似值的地方分配任意精度/复杂度。最后,仿真研究表明了该方法的有效性和适用性。

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