摘要
针对遗传算法收敛速度慢、传统BP算法易收敛于局部最优以及网络结构难以确定等问题,引进自适应遗传算法优化网络的权阈值以搜寻网络最优拓扑结构,并利用自适应遗传算法优化该网络的权阈值,建立基于改进BP网络的宏观管网状态模型.实例分析表明,改进模型具有较高的预测精度.
Simple GA has the shortcoming of slow convergence, while BP neural network is prone to the local optimum and its structure is usually difficult to be determined. In this paper, a real-coded self-adaptive GA was introduced to optimize the weight and threshold so that a binary-coded self-adaptive GA can find the best topological structure, and then a real-coded self-adaptive GA was presented to optimize the weight and threshold of BP network which has the best structure. A macroscopic state model of pipe network was developed based on improved BP neural network. Case study shows that the improved model has higher prediction accuracy.
出处
《水利水电技术》
CSCD
北大核心
2004年第10期61-63,共3页
Water Resources and Hydropower Engineering
基金
国家自然科学基金资助项目(50078048).
关键词
BP网络
自适应遗传算法
管网状态估计
BP neural network
self-adaptive genetic algorithm
state estimation of pipe network