【摘要】 根据径向基函数神经网络 (RBFNN)具有很强的非线性逼近能力的优点 ,本文采用RBF网络模型进行自由曲面重构 ,建立了适应于曲面重构的径向基函数网络模型 ,讨论了基函数对重构曲面连续性的影响 ,并与多层感知器神经网络的性能进行对比。理论分析和仿真实验结果表明 :常用的几种径向基函数重构的曲面都具有很好的连续性 ,径向基函数网络用于曲面重构 ,不论是在拟合精度 ,还是网络的训练速度都明显优于多层感知器网络 ,具有一定的实用价值更多还原

【Abstract】 A novel method for surface reconstruction by radial basis function (RBF) neural network is proposed. An appropriate RBFNN model is constructed. The relationship between basis function and property of constructed surface is discussed. Theory analysis and the experiment results show that the surface constructed by some commonly used basis function is smooth. The precision and the training speed of RBF for surface reconstruction are superior to that of MLP. This approach is valuable for practical application.更多还原