Explore MATLAB source code curated for "预测" with clean implementations, documentation, and examples.
Utilizing the latest 100 periods of Double Color Ball lottery draw numbers, this approach employs genetic algorithm to optimize BP neural network parameters for enhanced prediction accuracy
RBF-based neural network prediction program package (compressed archive), no extraction password required! Includes complete code implementation with radial basis function architecture.
This self-developed neural network solution for electric load forecasting includes complete datasets and functional code implementation. The program is ready-to-run and features data preprocessing, model training, and prediction visualization components.
MATLAB implementation of hybrid prediction model combining Particle Swarm Optimization and Grey System Theory with high accuracy performance
Implementation of Kalman Filter Algorithm for Target Trajectory Prediction in Radar Applications with Code Integration
Chaotic time series prediction program featuring multi-step prediction functions and a main forecasting routine with enhanced algorithmic capabilities.
A functional MATLAB program that performs empirical mode decomposition on time series data and implements predictive modeling using least squares support vector machines (LS-SVM)
This MATLAB implementation of Gray Model prediction allows users to specify the number of forecast years through a simple graphical interface input. The program utilizes GM(1,1) modeling methodology for time series forecasting.
Implementing Autoregressive (AR) modeling for oil price analysis and forecasting, incorporating BIC criterion for order selection and least squares method for parameter estimation with code implementation approaches
This MATLAB code was developed during mathematical modeling for time series prediction using Backpropagation Neural Networks. The implementation is fully functional and includes MATLAB-formatted data for easy validation. The code features detailed comments explaining key algorithm components, making it particularly suitable for beginners learning neural network implementations.