A Developmental Study of the Free-formed Surface System Using the Neural Networks

本研究的第一个目的是确定布朗表面的参数，它可以处理的表面特征与使用的张力和偏压控制程序。采用神经网络中的反向传播方法来确定控制值。曲面的特征有其相应的控制值。这些值由神经网络学习，并在学习后应用于表面控制。实现了五层反向传播算法。实验结果表明，这种尝试为曲面设计带来了良好的效果。在研究反向传播方法的性质时，注意到反向传播方法本身具有插值函数。这意味着，具有5层的反向传播方法能够直接用于插值和设计的表面。为了确定这些性质，数值实验试图收集他们的行为，利用自由曲线插值。作为曲线拟合，它给我们一个很好的结果。在不久的将来，我们计划将这一结果应用于自由曲面的设计。

The first purpose of this research is to determine parameters of Brown surface, which can handle the surface features with the use of tension and bias control procedures. The back propagation method, one of the neural networks, is employed for determining the control values. The feature of surface have their related control values. These values are learned by the neural networks, and they are applied to surface control after leaning. The five layers back propagation method is implemented. Experimental surface designs show that this attempt introduces good results into surface designing.In investigating properties of the back propagation method, it is noticed that the back propagation method itself has the interpolation functions. This means that the back propagation method with 5 layers is able to be directly used for interpolating and designing the surfaces. In order to make sure of these properties, numerical experiments are attempted to collect their behaviors for making use of free-formed curves interpolation. It give us a good result as a curve fitting. In near future, it is planed to apply this result to design free-formed surface.