CAJ | 학술논문

构造了一种带多个核函数的结构自适应径向基函数神经网络（RBF NN）降水预报集成模型。该网络结构同时使用6个核函数，采用基于输入输出全部信息的模糊相似矩阵的平均矩阵元法自动确定隐节点数，自适应地生成RBF神经网络集成个体，最后建立多元回归模型集成。对日本的细网格资料数据建立平均日降水量预报模型，利用MATLAB进行仿真实验，结果表明，该模型预报性能明显优于同期中国气象局的T213（中国气象局的全球中期天气数值预报产品预报值）降水预报，可为气象预报研究提供新思路，为降水预报决策的制定提供重要的参考。
구조료일충대다개핵함수적결구자괄응경향기함수신경망락（RBF NN）강수예보집성모형。해망락결구동시사용6개핵함수，채용기우수입수출전부신식적모호상사구진적평균구진원법자동학정은절점수，자괄응지생성RBF신경망락집성개체，최후건립다원회귀모형집성。대일본적세망격자료수거건립평균일강수량예보모형，이용MATLAB진행방진실험，결과표명，해모형예보성능명현우우동기중국기상국적T213（중국기상국적전구중기천기수치예보산품예보치）강수예보，가위기상예보연구제공신사로，위강수예보결책적제정제공중요적삼고。
This paper constructs an integrated model of radial basis function neural network (RBF NN)with self-a-daptive structure of multi-kernel function on precipitation forecast. The network structure using six nuclear functions at the same time adapts the method of mean matrix element based on the fuzzy similarity matrix of the input and output information and automatically ensures the number of hidden nodes. The generated RBF neural network ensembles indi-vidual and finally establishes integrated model of multiple regression. It also establishes the mean daily precipitation forecast model on Japanese fine grid data and does simulation experiment by using the method of MATLAB. The result shows that the predictability of this model is obviously superior to the T213 (the forecast value tested by the global me-dium-range numerical forecasting product of China Meteorological Bureau),which can provide new ideas for the study of weather forecast and important references for the decision making of the precipitation forecast.