【摘要】 提出一种基于属性重要性的粗糙RBF神经网络模型,该模型不仅具有明确的生物意义和物理意义,而且简化了拓扑结构,减少了运算量和成本.实际应用结果表明,这种粗糙RBF神经网络在油水层识别中效果显著,其学习训练速度和拟合精度远优于传统的RBF网络算法.更多还原

【Abstract】 A model of rough radial basis function(RBF) neural network with attribute significance is presented.It not only has evident physical and biologic meanings,but also can simplify topology structure,and decrease operation and cost.The application example shows that the effect in oil-water layer recognition is very good,and this algorithm is superior to the traditional one at fitting precision and training rate.更多还原