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.