机构地区: 西安交通大学电子与信息工程学院
出 处: 《控制与决策》 2006年第7期821-824,828,共5页
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. 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.
关 键 词: 粗糙 神经网络 粗糙集 属性重要性 油水层识别
领 域: [自动化与计算机技术] [自动化与计算机技术]