Elman Neural Network Regression Model with Genetic Algorithm Optimization
Elman Neural Network Regression Model Implementing GA-Optimized Weights and Thresholds with Practical Code Implementation Details
Comparison of Results: Standard RBFNN vs. GA-Optimized RBFNN
1. Optimize various weights in the RBFNN using a Genetic Algorithm (GA) implementation with fitness function evaluation and population evolution; 2. Perform function approximation/tracking using the RBF neural network with Gaussian basis functions and weighted summation; 3. Comparative testing and performance analysis between standard RBFNN and GA-optimized RBFNN using metrics like MSE and convergence speed.
GA-Optimized RBF Neural Network with Implementation Analysis
RAR archive containing analytical code for GA-optimized RBF neural networks featuring genetic algorithm implementation and neural network parameter optimization
MATLAB Implementation of RBF Neural Network with PSO and GA Optimization Comparison
Comparative Analysis of Radial Basis Function Neural Networks using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) Optimization Methods