摘要
提出采用粗糙神经网络预测坡面雨滴溅蚀量。用粗糙集方法中条件属性与决策属性相对依赖的概念约简某雨滴溅蚀量试验中的冗余信息,去掉了坡度、雨强、水深、单宽流量4个试验指标中水深和单宽流量两个指标,建立了以坡度、雨强为输入,溅蚀量为输出的2?5?1的粗糙神经网络模型,简化了神经网络的结构,减少了网络的训练时间。实例计算中信息约简后预测值与试验值线性回归的相关系数大于未约简时的相关系数值,计算速度也有所提高。实例计算表明,粗糙神经网络为坡面雨滴溅蚀量预测研究提供了一种有效可行的算法。
It is presented to predict slope splash-erosion for rain drops based on rough neural network. The redundancy information is reducted by the relative dependability between condition attribute and decision attribute of rough sets. The test indexes such as water depth and flux are deleted. The 2-5-1 rough neural network is established in which input parameters are gradient and rain intensity while output parameter is splash-erosion quantity. The frame of neural network is predigested. The train time of neural network is decreased. The reduced sloping field rain drops splash-erosion linear regression correlation coefficient of prediction and experiment is larger than that had not been reduced. The constringency speed is faster than that had not been reduced. The example calculation indicates that the rough neural network is an efficient and feasible algorithm to forecast sloping field rain drops splash-erosion.
出处
《岩土力学》
EI
CAS
CSCD
北大核心
2006年第8期1425-1428,共4页
Rock and Soil Mechanics
基金
教育部博士点基金项目(No.20030533043)
关键词
粗糙集
神经网络
雨滴
溅蚀量
rough sets
neural network
rain drops
splash-erosion
