Application of Fuzzy Clustering and Later Regression in Time Series Prediction of Airport Noise
Under the prediction of airport noise,two kinds of methods of time series prediction,based on fuzzy clustering and later support vector regression,are proposed. One is Fuzz C-Means and later regression,which first makes the samples within the same cluster,and then the similar samples are used to regress and predict. The other is Shadowed Rough-Fuzzy C-Means and later regression,which first partitions the cluster into the lower bound and the boundary region,where the samples in the lower bound contribute more to the cluster than those in the boundary region,and thus makes the samples within the same region of the same cluster. And then the similar samples in the same one are used to regress and predict. Two frequently-used datasets and measured data of an airport in Beijing are selected in the experiment. Results demonstrate that this forecasting technique of pre-clustering and later regression,proposed in this article,is more accurate than that from regression.