一种改进的自适应神经网络及其在随机形状上的应用

Can-Lin Mo, J. Tan
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引用次数: 0

摘要

提出了基于自适应神经网络的随机形状生成方法。自适应神经网络是在随机变形过程中从任意规则几何形状进行训练的。利用自适应学习方法可以将规则形状变为不规则形状,增强了系统的全局可控性和局部可控性。通过对传统自适应神经网络算法的改进,将确定性和随机性充分结合起来,使模糊可控性和模糊可调性更容易、更简洁地控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved adaptive neural network and its application on random shape
The random shape generation method is put forward based on adaptive neural networks. The adaptive neural network is trained from an arbitrary regular geometric shape during the random deformation process. Thus, the regular shape can be changed to an irregular one with the adaptive learning method, and the global and local controllability can both be enhanced. With an improvement on the traditional adaptive neural network algorithm, certainty and randomness can be fully combined, so that fuzzy controllability and adjustability can be dominated easily and concisely.
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