Output Prediction Model in Fully Mechanized Mining Face Based on Support Vector Machine

摘要

Support Vector Machine is a new machine learning technique developed on the basis of Statistical Learning Theory, which has become the hotspot of machine learning because of its excellent learning performance. Based on analyzing the theory of support vector machine for regression (SVR), a SVR model is established for predicting the output in fully mechanized mining face, and then realizes the model by programming based on Mat lab, finally, compared with genetic neural network prediction model. It shows that SVM has a higher accuracy of prediction than GNN, which proved the validity and practicality of the model.