Genetic Algorithm Optimization of BP Neural Networks

This example demonstrates nonlinear function fitting by applying optimal individuals obtained from genetic algorithms to BP neural networks. The implementation involves MATLAB programming for genetic algorithm optimization of BP neural network models, utilizing key functions like ga() for evolutionary optimization and train() for network training.

Nonlinear System Modeling Using BP Neural Networks

This implementation demonstrates BP neural network-based nonlinear function fitting in MATLAB, covering data preprocessing, network architecture configuration, model training with backpropagation algorithm, and performance validation techniques.

Support Vector Machine Nonlinear Regression

This MATLAB source code provides a generic implementation of Support Vector Machine (SVM) nonlinear regression, applicable for linear regression, nonlinear regression, nonlinear function fitting, data modeling, prediction, and classification scenarios. The implementation utilizes kernel functions for handling nonlinear relationships and includes optimization algorithms for efficient parameter tuning.