伴随时空特性的雷电预测BP-ANN模型研究

为提高雷电预测模型的准确率和学习性能,提出一种基于增量学习和时空特性的雷电预测BP-ANN二项分类器。通过增量方式和依据数据的时空特征进行历史数据的学习,建立多种BP-ANN模型,分别对新的数据进行预测分类,然后采用多数投票方式确定新数据的类别。分别构建基于增量学习的BP-ANN模型、基于时空特性的BP-ANN模型以及结合基于增量学习和时空特性的BP-ANN模型这3种雷电预测模型,并在真实雷电数据集上进行预测准确度和学习性能的测试,结果表明了增量学习、时空特性以及二者结合的优劣。

Research on BP-ANN Models of Lightning Prediction with Spatio-temporal Characteristics

In order to improve the accuracy and learning performance of the lightning prediction model, a BP-ANN binomial classifier of lightning prediction based on incremental learning and spatio-temporal characteristics is proposed. It makes a study of historical data by incremental approach and according to spatio-temporal characteristics of data, builds many BP-ANN models, classifies the new data respectively, and then uses the majority voting to determine the category of the new data. This paper constructs three kinds of lightning prediction models, BP-ANN model based on incremental learning, BP-ANN model based on spatio-temporal characteristics, and BP-ANN model combined both. The accuracy and learning performance are tested on real lightning data set, the results show the advantages and disadvantages of incremental learning, spatio-temporal characteristics and combination of both.