摘要
针对遗传算法的不足 ,利用改进的遗传算法 ,结合性能优于 BP网络的径向基函数神经网络 ,并进行网络优化 ,建立了黄河流域需水预测模型 ,拟合预测结果表明 ,该模型能有效提高预测精度。
Due to the defect of genetic algorithm, this paper applies extended genetic algorithm to establish the model of water requirement prediction of Yellow River basin combined with radial basis function neural network that is more superior to BP neural network.And then the model is used to predict the water requirement after it is optimized.Results of prediction indicate that the model can improve predicting ability and the accuracy efficiently.
出处
《水土保持学报》
CSCD
北大核心
2002年第3期83-85,97,共4页
Journal of Soil and Water Conservation
基金
国家重点基础发展规划 (973 )项目"黄河流域水资源演化规律与再生维持机理"(G1990 43 60 8)资助
关键词
遗传算法
径向基函数
神经网络
黄河流域
需水预测
genetic algorithm
radial basis function
neural network
Yellow River basin
water requirement prediction
