| Since the 90's of 20 centuries, Some the Neural Network forecast mold were applied in domestic and international atmosphere course. With the research be made a thorough and careful, the researcher discovered an important problem in the actual weather forecast application, which the Neural Network beginning connection weights, the network construction, learning factor and momentum factor were hard certain. Because the researcher need train many times so that all kinds of parameters can be definite, the Neural Network become over-fitting and the serious influence the Neural Network generation ability. This problem will limit the Neural Network in the actual forecast application ([1-8]). The research of that problem not only relate to whether to can go deep into the Neural Network business forecast research in atmosphere course but also is the key technique in the theories research.In recent years, the numerous scholar in the domestic and international combinate the Genetic Algorithms with the Neural Network, make use of the GAs optimized the NN. Sun Xiaoguang and Fu Yunyi([10],1998)optimized the BP network connection weights using genetic algorithm and the scheduling problem on hot strip mill has been solved. Li Mingqiang and Xu Boyi ([11],1999) combine the GAs and the NN for solving short term earth quake forecasting problem, design a novel method of using GAs to train connection weights of NN. LI Zuoyong and Peng Lihong([14],2003)make use GAs optimized parameter model and set up storm rainfall intensity formulae of different repeat periods in Beijing suburban. Rao Wenbi and Xu Rui([13],2003) studied the problem of structure damage identification based on combination of GAs and NN, the study the possibility of solving the problem of structure damage with this model. Li Fanbin and Cui Peng ([14],2003)set up GNN Debris flow model, which s the combination of NN and AGs. The model is suitable for analyzing the activity of debris flows. But current in the atmosphere course no one study the Neural Network constructers and connection weights and establish the short-term weather forecast model.The paper aims at some weakness the BP(Back-Propagation) Neural Network in the actual application, such as the astringency slow, easily falling into local solutions concerning bigger search space and complex function, another the Neural Network beginning construction and connection weights having no way in definition. These affect biggest the Neural Network generalization ability and limit in the actual weather forecast application. Optimized the neural network and connection weights by means of genetic algorithm, reserved the best individual in evolution process, this method be established up the research of climate prediction the first forecast model. Because GAs can search the superior solution region in evolution process and don't make certain situation the superior solution exists. The paper set up the second BP Neural Network forecast model based GAs, using training sample to choose the best network connection weights and network structure form the result of evolution.1. The method can overcome the defects of unsteady and falling into local solution and validly increase generalization ability. The applied example is setted up a climate forecast model, with monthly mean rainfall the whole area of Guangxi in April and the predicted factors of previous 500hpa height and sea surface temperatures. Predictive capability between the new model and linear regression model for the predictors is discussed based on the independent samples. Evidence suggests that the prognostic ability of the model with high accuracy and stability is superior to that of a traditional method.2. The method forecast ability and accuracy increase as sample be increased, embody strongly learning ability.3.The paper specially put forward to make use of the training sample from the result of evolution and choose the best network connection weights and network structure, this method brings their advantage into full play and combine t...
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