A Multiple Nuclear Radial Basis Function Neural Network Integrated Model Of Precipitation In Guangxi

To improve the accuracy of precipitation forecast of meteorological prediction field is a very important topic. Surface topography, the molecules of air, humidity and clouds, and other factors, many years of precipitation forecast hard, daily precipitation variation is a complex nonlinear process. At present, including the Chinese Meteorological Bureau T213mode and a number of precipitation prediction models have been proposed, each of these models as far as possible to find precipitation and the measured physical quantity forecast factor between certain nonlinear relationship, hope to establish a historical data based stable, accurate, good generalization performance and the applicability of the case-based reasoning model. Since the nineteen ninties, along with neural network technology in Atmospheric Science in a wide range of applications, the neural network with its good adaptive learning ability and nonlinear processing ability, in weather forecast effect is increasingly remarkable. But the neural network technology due to the absence of a complete theoretical system, the user experience on application effect plays a decisive role. Actual application, the researchers because of the lack of prior knowledge, in order to determine the appropriate network parameters, often can not avoid a time-consuming and laborious experiments. In many cases, even for a problem by using the method of the same, different operators, the results often differ very far. Even on a number of data set up parameters, another group of data the result is not good, even not applicable, the parameters of the model to experimental exploration to determine. These are largely restricted to the neural network model in practical application.Radial basis function neural network (RBF network) is a three layer feedforward neural network. Center, the number of hidden neurons and width selection is suitable or not directly affect RBF neural network performance. The hidden layer nodes is too little, cannot satisfy some sample learning needs enough connection weights combination number, affecting the network approximation accuracy; hidden layer nodes number, then after studying the generalization ability of the network becomes poor, may lead to overfitting phenomenon, network response slow. In practical application, the random selection of a certain number of input mode; or in a specified number of cases, using K means clustering algorithms to select the RBF Network Center; the gradient descent learning algorithm, the network error adjustment data centers, widths and weights, the network performance to achieve better. But the algorithm before learning needs to determine the center number and width; orthogonal least squares (Orthogonal least squares, OLS) RBF network learning algorithm, can be in the process of training according to the hidden nodes on the network size of error, the hidden nodes to adjust, to obtain a suitable hidden nodes, relative to the fixed center learning algorithm, OLS does not need to determine the network node numbers of hidden layer, solves the hidden nodes is difficult to identify the problem. But the need to identify in advance the extended constants of the hidden nodes, i.e. the width. The algorithm is universal and has been applied, the disadvantage of requiring a pre-determined hidden nodes or width and other parameters, and these parameters is not a complete and effective method, often in order to obtain the suitable parameter values required a great deal of trouble to work hard.Due to the influence of many factors, many precipitation impact factor makes the model training and prediction of the larger size, the training time of the network long, slow convergence speed, reduces the model to predict the performance of. Therefore it is necessary for these dozens or hundreds of precipitation forecast factor for dimensionality reduction. In the influence of precipitation factor of numerous data, different kernel functions for each input factor is not the same degree of impact, how to choose a good kernel function to weigh these factors, help to improve the accuracy of precipitation forecast model.Based on the radial basis function neural network (RBF network) of the node numbers of hidden layer, the center and width of the difficulty, and different kernel function with different input variables affecting the degree is not a factor, input of numerous and large amounts of data problems. To build a common and effective precipitation forecast model. The research object of this paper is based on the radial basis function neural network (RBF network) precipitation forecast model, the main research work is as follows:(1) in response to precipitation influence factor of numerous problems, firstly, using kernel principal component analysis (KPCA) for screening major precipitation influence factor, experimental results show that the KPCA dimensionality reduction to extract the factor is feasible and effective.(2) in the radial basis function neural network (RBF network) and the number of hidden layer nodes, centers and widths are difficult to identify problems, in order to improve the network performance, analysis of the common learning algorithms, proposed one kind based on the fuzzy clustering algorithm to determine the hidden nodes of RBF optimization algorithm. Firstly using fuzzy clustering algorithm to determine the sample data of hidden nodes; and then the K mean clustering algorithm to classify, identify the hidden node centers; again with the hidden nodes of minimum distance between extended as constants, i.e. the width; finally, in guarantee minimum network performance metric, to realize linear interpolation, the use of the least squares algorithm to train the network, determining the output layer of the network weights, so that network performance in some sense optimal. The algorithm study does not need to determine the hidden nodes, center and width, solved these parameters problem.(3) a mixture of six RBF kernel functions, build more nuclear radial basis function neural network integrated model, on the6forecast model for simple average, stepwise multiple regression forecasting integration, thereby taking into account the different kernel functions to those input variables.(4) in Guangxi in May3area daily precipitation real data prediction experiment, experimental results show that, the model has good generalization performance, the forecasting accuracy is higher than that of T213precipitation prediction model, which has the value of application.