关键词: 支持向量机, 粗糙面, 神经网络, 均方根高度, 相关长度, 反演

Abstract: Support vector machine and neural network theory and internal network training differences of them are studied.Root mean square height and correlation length of Gauss rough surface are inversed by support vector machine and neural network,respectively.Simulation results and inversing errors show that in the case of small numbers of rough surface sample inversion of support vector machine are better than that of neural network,while in the case of sufficient numbers of rough surface samples inversion accuracy of neural network increases and time of inversion by neural network is much less than that of support vector machine.

Key words: support vector machine, rough surface, neural network, root mean square height, correlation length, inversion