RBF Neural Network Source Code Implementation

This resource provides complete source code implementation for Radial Basis Function (RBF) neural networks along with comprehensive MATLAB modeling and simulation procedures. We specifically highlight this code's primary application in power load forecasting - a critical task that enables the power industry to better plan and manage electrical resources. The implementation utilizes MATLAB's neural network toolbox functions including newrb() for network creation and sim() for simulation, featuring Gaussian radial basis functions as activation nodes. Through RBF neural network modeling and MATLAB simulation, our solution delivers accurate prediction results, allowing power companies to effectively forecast future load demands and make corresponding adjustments and decisions. The source code and modeling techniques are based on the latest research algorithms and have undergone rigorous testing and validation. Our implementation includes configurable parameters for spread constants, maximum neuron counts, and mean squared error targets to optimize network performance. We hope our tools and technology can provide effective support and assistance for load forecasting in the power industry.