| Summary: | 碩士 === 國立暨南國際大學 === 資訊管理學系 === 107 === This study mainly discusses whether data preprocessing is helpful for training of Deep Belief Networks(DBN). Using air pollution data, we first lower the noise by preprocessing and fill the missing values. Then we use logarithm(LOG), verification, stepwise regression, wavelet analysis to change the data structure, or eliminate some of the independent variables to lower the noises. We use one of metaheuristic algorithms, namely Genetic Algorithm(GA), to help searching parameters, decide network structure to minimize human involve, and to prevent local minimum solution. We discuss whether data preprocessing is helpful for forecasting in terms of DBN using 12-hour and 24-hour air pollution forecasting models. But massive missing values in a dataset is not trustable neither in multivariate regression nor time series forecasting in terms of performance. The proposed preprocessing methods combined with DBN improves the performances of forecasting.
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