摘要
为避免库岸稳定性评价法的随意性和不确定性,尝试采用具有处理非线性关系功能的人工神经网络方法进行库岸稳定性分级,为此构建了15-31-4结构的三层BP网络。该网络采用BP弹性算法,同时对初始权值和阀值进行了优化,实现了网络的非线性映射,并有着极快的收敛速度。用该BP网络对三峡水库的上游段库岸进行了稳定性等级判断,其结果与常规计算方法所得的结果基本相似。
In order to avoid the random and uncertainty of the assessment method of reservoir bank stability , the artificial neural network（ANN） method with the function of disposing the nonlinear relation was applied to judge the grade of stability. A three-layer BP network model was established with 15 input nodes, 31 nodes in hided layer and 4 output nodes . In this network model, the BP elastic algorithm（RPROP）was adopted and the initialized weight value and valve were optimized and this model has realized the nonlinear reflection of the network and has the fast speed of convergence. This established method of the three layers of BP network is an effective method worthy of popularizing. Case study in the upstream banks of reservoir of Three Gorges shows that the judgment of stability grade of reservoir bank with this BP network mentioned above was similar to the results of conventional calculation methods.
出处
《吉林大学学报（地球科学版）》
EI
CAS
CSCD
北大核心
2007年第3期564-569,共6页
Journal of Jilin University：Earth Science Edition
基金
国家自然科学基金项目(40472136)
教育部资助优秀年轻教师基金项目(120413133)
关键词
人工神经网络
BP弹性算法
库岸稳定性
artificial neural network
the BP elastic algorithm
the stability of reservoir bank
