Risk Assessment Modeling of Urban Railway Investment and Financing Based on Improved SVM Model for Advanced Intelligent Systems

Abstract

A risk assessment method for urban railway investment and financing based on an improved SVM model under big data is proposed. First, the inner product in the traditional SVM is replaced by a kernel function to obtain a more accurate non-linear SVM, and a classifier with high classification accuracy is achieved by finding the optimal separating hyperplane. Then, a risk index system is constructed based on the grounded theory combining with intuitionistic fuzzy sets, interval intuitionistic fuzzy sets, weighted averaging operators and the distance measure, and the selection method of assessment indexes is analyzed based on the statistical methods. Finally, the SVM model with fuzzy membership is obtained by fuzzifying the input samples of the SVM based on the given rules of fuzzy membership design. The results show that the maximum relative error between the final test results and the actual value is 0.316%, and the minimum relative error is 0.133% with three different test sets being tested in the proposed method, which can accurately assess the investment.