The impact of data normalization on tropical cyclone track forecast in South China sea

In the application of Tropical Cyclone Track (TCT) forecast in South China Sea (SCS), pure linear neural network (PLNN) is used as the expert in the committee machine model, and it partly determines the model output. Data normalization is one of the most important factors, which affect the performance of the individual expert net. This paper aims to find how much data normalization affects the convergence process and forecast accuracy of the independent samples, and whether data normalization could bring reasonably accuracy and diversity that the committee machine model requires.