随机形状变形生成的自适应神经网络控制法
Adaptive Neural Network Control for Generating Random Shape
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摘要: 将任意给定的确定性规则形状作为自适应神经网络的学习对象,用自适应学习的方法改变规则形状的形成规则,在没有加入随机移位扰动量的情况下,使规则形状从"规则"向"非规则"转变.改进了传统自适应神经网络的算法过程,增强了随机形状的局部和整体形态特征的可控性,拓宽了自适应线性神经网络在随机形状造型中的应用范围,且方法简明、易于实现.Abstract: The adaptive neural network is trained to incorporate random deformation process into the given regular determined shape according to certain rules. Thus, the regular shape can be changed to irregular one with the adaptive learning approach, and both the global and local shape controllability can be enhanced. With improvement on the traditional adaptive neural network algorithm, the certainty and randomness can be fully combined, and the fuzzy controllability and adjustability can be achieved easily and concisely.
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