Marathon Grades Time Series Forecasting Based On Improved Radial Basis Function Neural Network

Marathon grades time series forecasting is very significant for the development of this sport. In order to obtain the excellent Marathon grades time series forecasting results, improved radial basis function neural network is applied to Marathon grades time series forecasting. When constructing a radial basis function neural network, it is important to determine the network structure and the network parameters. How to determine the optimal structure of RBFNNs is very important. In this paper, genetic algorithm is applied to the radial basis function neural network to search for the optimal combination of the parameters of radial basis function neural network. The experimental results show that the Marathon grades time series forecasting accuracy of genetic algorithm-radial basis function neural network is higher than that of-radial basis function neural network.