关键词: 人工神经网络, 矿产资源预测, 支持向量机, 核函数

Abstract: Traditional mineral forecasting models are more by a certain statistics supposition for premise，but such as supposition event in practical application totally doesn’t exist.So nonlinear science with mineral resources forecasted unified is the developing direction.Artificial Nerve Network（ANN） can establish a non-linear reflection relation between the input and output，has good fault-tolerant，self-learning and adaptability ect.But it has a slow convergence，a local optimum，the uncertainty network structure and can not resolve the VC issues ect.SVM has the strict guidance of general theory，the powerful mapping capabilities of nuclear non-linear function and many advantages which ANN has not，such as no local minimum and problem of dimension.Based on this，this paper proposes SVM mineral forecasting methods，applying SVM to the mineral resources forecast，and compares and analyzes SVM，BP and RBF forecasting methods.The results show that SVM has a better ability to learn and generalize and has made good results in the prediction of mineralization and has a better performance in predictable performance than the traditional neural network and are close to the actual value.

Key words: Artificial Nerve Network（ANN）, mineral forecasted model, Support Vector Machine（SVM）, kernel function