Support Vector Machine Nonlinear Regression General Implementation

In this paper, we conduct a comparative study of nonlinear regression using Support Vector Machines (SVM) and BP Neural Networks. First, we implement a general MATLAB program for SVM nonlinear regression, utilizing MATLAB's built-in functions like `fitrsvm` for regression tasks with configurable kernel functions (such as radial basis function or polynomial kernels) to capture complex nonlinear patterns. SVM is a powerful machine learning algorithm that identifies and fits intricate nonlinear relationships through kernel trick implementations, enabling better understanding and prediction of data patterns and trends. Next, we employ BP Neural Networks for nonlinear regression, implementing the backpropagation algorithm through MATLAB's Neural Network Toolbox functions like `feedforwardnet` and `train` with adjustable hidden layers and activation functions. This network adapts weights and biases through gradient descent optimization to fit data patterns effectively. We will compare the performance and accuracy of SVM and BP Neural Networks in nonlinear regression tasks, evaluating metrics such as mean squared error and computational efficiency to determine which method better suits specific problems and datasets. This research provides insights into the strengths and limitations of nonlinear regression methods, offering valuable references for future research and practical applications.