Support Vector Machine-Based Nonlinear System Identification Program

In the MATLAB runtime environment, Support Vector Machines (SVM) can be employed to develop nonlinear system identification programs. Support Vector Machines represent a widely-used machine learning methodology that constructs nonlinear models through training samples and predicts unknown data patterns. The implementation typically involves MATLAB's built-in SVM functions (like fitrsvm for regression or fitcsvm for classification) or third-party toolboxes such as LIBSVM. For nonlinear system identification, the program would utilize kernel functions (e.g., radial basis function or polynomial kernels) to map input data into higher-dimensional feature spaces where linear separation becomes feasible. Using SVM for nonlinear system identification facilitates deeper understanding of system behaviors and provides foundations for system modeling and control strategies. Prior to executing this program, users must install and configure the MATLAB environment while preparing corresponding training datasets and testing data. The implementation workflow generally includes data preprocessing, SVM model training with parameter optimization (like box constraint and kernel scaling), and model validation through cross-validation techniques. Upon program execution, users obtain a relatively accurate nonlinear system model suitable for further analysis and practical applications, with potential extensions to real-time system monitoring and predictive control implementations.