神经网络在采煤沉陷区水系治理方案中的应用  

Application of Neural Networks in Project Optimization of Water System Management in Mining Subsidence

作　　者：谭波波[1] 沈思良[2] 吴志红[2] 王列平[1] 

机构地区：[1]安徽理工大学地球与环境学院,安徽淮南232001 [2]淮南矿业集团有限责任公司资源环境管理部,安徽淮南232001

出　　处：《安徽理工大学学报（自然科学版）》2009年第2期13-16,共4页Journal of Anhui University of Science and Technology:Natural Science

摘　　要：在采煤沉陷区水系治理方案优选中,采用Hopfield神经网络求解优化问题原理,将方案优选问题映射到Hopfield神经网络,建立能量函数模型,求解能量达到最小值时的置换矩阵,由置换矩阵得到方案优选排序,决策者参考方案优选排序结果确定拟采用的水系治理方法。In project optimization of water system management in mining subsidence, principals of problem optimal solution by Hopfield neural networks was applied. Project optimization issue was mapped into Hopfield neural networks and energy function model was established. Permutation matrix for minimum energy was obtained, by which project optimization order is obtained. Decision maker determines which water system management project is used on the basis of optimization order.

关 键 词：HOPFIELD神经网络 置换矩阵 水系治理 方案优选 

分 类 号：TV212.52[水利工程—水文学及水资源]