SVM Prediction Interface for MATLAB Implementation
User interface for Support Vector Machine predictions using MATLAB, featuring data handling and model configuration capabilities for practical machine learning applications.
Object Position Detection and Prediction using Kalman Filter
GUI-based implementation for video input reading, image acquisition, and Kalman filter-based object position detection and prediction
LMS Adaptive Algorithm with Computerized Experimental Adaptive Prediction
MATLAB Simulation Example of LMS Adaptive Algorithm for Adaptive Prediction with Code Implementation Details
A Neural Network-Based Stock Prediction System
This is a robust stock prediction program utilizing neural networks, featuring high accuracy, powerful predictive capabilities, and excellent curve-fitting performance for identifying market patterns
BP Neural Network for Short-Term Electric Load Forecasting
BP neural network approach for short-term electric load forecasting using normalized input data to predict daily loads based on weekly patterns
Program Implementation of Short-Term Traffic Flow Prediction Using Kalman Method
Implementation of a short-term traffic flow prediction system based on Kalman filtering algorithm with historical and real-time data analysis capabilities
Support Vector Machine Prediction Program
This MATLAB-based Support Vector Machine program is primarily designed for predictive analysis, implementing SVM algorithms for both classification and regression tasks with configurable parameters and result visualization capabilities.
Time Series Prediction Using Neural Networks Optimized with Genetic Algorithm
Implementation of genetic algorithm-optimized neural networks for time series prediction. The genetic.m interface function provides straightforward configuration, allowing direct modification of neural network parameters. Users can easily substitute their own data files by updating the load function call, enabling efficient adaptation to diverse datasets.
Elman Neural Network Prediction for Power Systems
An Elman neural network-based prediction program designed for short-term load forecasting in electrical power systems, featuring recurrent network architecture for time-series analysis
Chaotic Time Series Prediction Using Sugeno-Type ANFIS
Implementation of chaotic time series prediction using Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS).