Simulation Comparison Between BP Neural Networks and RBF Neural Networks with MATLAB Implementation

I conducted a comparative simulation study examining the performance differences between BP neural networks and RBF neural networks. The project package includes four complete MATLAB scripts that are executable in the MATLAB environment. These implementations cover key aspects such as network initialization, training processes, and performance validation. Through my research, I identified distinct advantages and limitations of both network architectures. BP neural networks demonstrated superior performance in scenarios requiring deep feature learning and complex pattern recognition, implemented through gradient descent optimization with error backpropagation. Conversely, RBF neural networks proved more effective for faster convergence in interpolation problems and local approximation tasks, utilizing Gaussian activation functions and efficient center selection algorithms. The study also explores practical applications of neural networks in real-world problems. The MATLAB implementations include examples applicable to domains such as image recognition (feature extraction and classification), speech processing (pattern matching), and predictive analytics (time series forecasting). Each script demonstrates specific MATLAB functions including 'newff' for BP network creation, 'newrb' for RBF network construction, and various training functions like 'trainlm' for Levenberg-Marquardt optimization. This comparative simulation provides valuable insights for neural network selection and development strategies. The code includes performance metrics calculation, error analysis, and visualization components that help understand the trade-offs between training time, accuracy, and generalization capabilities of both network types. These implementations and findings aim to enhance understanding of neural network fundamentals while providing practical coding examples that can serve as foundations for more complex applications or research projects in machine learning and artificial intelligence.