Optimized Least Squares Support Vector Machine Implementation
MATLAB implementation for optimized least squares support vector machines with ready-to-use functionality and comprehensive features.
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.
Optimization of RBF Neural Networks Using Adaptive Genetic Algorithm
Comparative analysis of simulation results between adaptive genetic algorithm-optimized RBF neural networks and particle swarm optimization-optimized RBF neural networks, featuring directly executable MATLAB code implementations.
Genetic Algorithm Optimization for BP Neural Networks
Utilizing Genetic Algorithms to Optimize Neural Network Weights and Thresholds with Code Implementation
Multi-Objective Particle Swarm Optimization for Distributed Generation Siting
Implementation of optimal objectives using multi-objective particle swarm algorithm for distributed generation location optimization
MATPOWER: A MATLAB-Based Power System Power Flow Calculation and Optimization Tool
MATPOWER is a powerful MATLAB-based program for power system power flow calculation and optimization, offering comprehensive capabilities for electrical engineering applications.
Multi-Parameter Automatic Selection Optimization for Support Vector Machines
Support Vector Machine multi-parameter automatic selection optimization program! Machine learning and data mining tool implemented in MATLAB with automated parameter tuning algorithms.
Optimizing Wavelet Neural Networks Using Genetic Algorithm
Implementation of genetic algorithm-optimized wavelet neural network for function approximation with complete program structure and enhanced convergence performance
Genetic Algorithm Optimization for Support Vector Machine Algorithm
Application Background: The current standalone SVM exhibits limited recognition accuracy. This program employs genetic algorithms to optimize the SVM algorithm, enhancing its precision and predictive performance. Key Technology: GA-SVM optimization algorithm improves recognition accuracy and prediction reliability through parameter tuning and model adaptation.
Genetic Algorithm Optimized Neural Network Program
This MATLAB program implements neural network optimization using genetic algorithms