Comparison of Results: Standard RBFNN vs. GA-Optimized RBFNN
- Login to Download
- 1 Credits
Resource Overview
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.
Detailed Documentation
In this study, we implement the following three-step methodology to optimize RBF Neural Network performance and conduct comparative testing with non-optimized RBFNN:
1. Apply Genetic Algorithm (GA) optimization to tune RBFNN weights, including implementation of chromosome encoding for weight parameters, fitness evaluation using error minimization, and selection/crossover/mutation operations;
2. Utilize the RBFNN for function approximation tasks, involving Gaussian radial basis function computation and output layer weight combinations;
3. Perform comprehensive comparison testing between GA-optimized RBFNN and standard RBFNN, analyzing performance indicators such as tracking accuracy, convergence behavior, and generalization capability.
Through this systematic approach with proper algorithm implementation, we achieve significant performance enhancement in RBFNN optimization and obtain superior results in function approximation applications.
- Login to Download
- 1 Credits