Using the Data of Earthquake Magnitude Samples to Verify the Superiority of PCA-GSM-GRNN Model
We proposed the earthquake prediction model that is based on the general regression neural network(GRNN)optimized by the grid search method(GSM)and principal component analysis(PCA),which addressed the nonlinear relationship between earthquake magnitude and its impact indicators.Moreover,we used PCA to reduce the dimensionality of the impact indicators and applied the reduced principal components as the input vectors of the model,which required the earthquake magnitude as the output vector.We further used GSM to optimize the best parameters of the GRNN,and trained the model by learning samples to construct the earthquake magnitude prediction model based on PCA-GSM-GRNN.Finally,we applied the PCA-GSM-GRNN model to the test samples.The results showed that the accuracy of the PCA-GSM-GRNN model prediction was improved by 5.03%and 5.66%compared with the GSM-GRNN model and GRNN model,which indicates a good prediction performance.
万方数据
维普
