基于模糊神经网络和遗传算法的大坝安全监控模型

应用监控模型来监控大坝的工作性态是一条有效途径 ,但由于大坝工作条件复杂 ,影响因素繁多 ,为以精确的数学模型进行描述带来了很大的困难 ,而应采用从定性到定量的综合集成的方法 ,将专家知识、监测数据和各种信息与计算机软硬件技术结合起来 ,把坝工理论和坝工专家的经验结合起来对其进行研究。文中应用模糊神经网络和遗传算法等人工智能技术 ,依据专家的经验确定隶属函数 ,从而建立模糊神经网络预报模型 ,根据专家对实际情况的正确分析 ,对预报结果进行修正 ,达到进一步提高预报精度的目的

DAM SAFETY MONITORING MODEL BASED ON FUZZY NEURAL NETWORKAND GENETIC ALGORITHM

Dam performance monitoring by use of a monitoring model iseffective. Because of complicated work condition and many affecting factors, it is difficult to describe dam performance by use of a precise mathematical model. The problem is resolved by use of a qualitative and quantitative method. The method combines computer technology with experts' knowledge, monitoring data and information, and combines dam theory with experts' knowledge. A fuzzy neural network prediction model is built by use of the technologies of fuzzy neural network and genetic algorithm. According to experts' experience, a subjection degree function is established. The predicted value is corrected according to experts' analysis of actual condition,thus the precision of prediction can be improved.