关键词: RBF网络, OLS算法, 地质样品, 元素含量, 预测

Abstract: Artificial neural network has self-organization, self-study and non-linear approaching ability, of which RBF (Radial Basis Function) network is a kind of forward network constructed on the basis of function approximation theory. The study of this kind of network equals searching for the optimal fit plane for the training data in high dimensional space. The X ray florescent counting data of Panzhihua certified geological samples are normalized and classified by suing self-organization neural network. OLS algorithm of RBF network is adopted to forecast the Ti element content in the Panzhihua uncertified geological samples and the relative error between the forecast data and those of the chemical analysis is all smaller than 0.5%, an ideal result.

Key words: RBR network, OLS algorithm, Geological sample, Forecast