CAJ | 학술논문

采集大连某机场2004-2007年大雾、轻雾和无雾天气事件共186例,选取雾天气事件前期(前一日08:00,14:00,20:00(北京时)实测资料)的温、压、湿、风等要素指标为预报因子,基于学习向量量化神经网络(learning vector quantization,LVQ),采用逐级预报思想建立起某机场雾天气事件的预报模型.在网络训练过程中,动态调整网络神经元比例参数,提高模型的预报能力;采用根据检验准确率适时终止训练的"先停止"技术,有效提高了模型的泛化能力.预报试验表明:无论是拟合率还是独立预报准确率,模型均已达到较高水准,具有实际应用意义.
채집대련모궤장2004-2007년대무、경무화무무천기사건공186례,선취무천기사건전기(전일일08:00,14:00,20:00(북경시)실측자료)적온、압、습、풍등요소지표위예보인자,기우학습향량양화신경망락(learning vector quantization,LVQ),채용축급예보사상건립기모궤장무천기사건적예보모형.재망락훈련과정중,동태조정망락신경원비례삼수,제고모형적예보능력;채용근거검험준학솔괄시종지훈련적"선정지"기술,유효제고료모형적범화능력.예보시험표명:무론시의합솔환시독립예보준학솔,모형균이체도교고수준,구유실제응용의의.
The generating and dissolving of fogs are too complex for empirical and linear systems methods to forecast and these methods cannot meet the needs of flight training.To meet this end,a new fog predicting model is proposed based on learning vector quantization neural network.The forecasting model of fog weather events is established using sequential forecast idea,adopting principal component analysis (PCA) and learning vector quantization network too.186 cases of heavy fog,mist or fog-free weather events on a certain airport is studied.Temperature,pressure,moisture,wind and other elements observed at 08:00,14:00,and 20:00 the day before the foggy weather are selected as prediction factors.Based on Learning Vector Quantization neural network,the prediction model of airport foggy weather events is established using sequential forecast idea (fog versus fog-free,heavy fog versus mist),and the prediction factors can be simplified using the principal component analysis. In the network training process,the model forecasting capability is improved in accordance with fitting accuracy to dynamically adjust neurons sealing parameters of the network.Adopting"to stop"technology of the timely termination training in accordance with testing the accuracy,generalization ability of the model is effectively improved.Forecasting experiments show that,the proposed model can effectively distinguish fog,mist and fog.Both the fitting rate and the forecasting accuracy are satisfactory so the model is practical.