Description: PSO-RBFNN algorithm optimization procedures Step 1. Sample data normalization treatment, about input and output normalized to [-1,1] interval 2. To determine the center and width of the RBF network 3. To the fitting error of the mean square roots as a performance index, using the PSO algorithm to optimize RBF network output layer to hidden layer connection weight matrices
- [rbf_Kmeans] - a K-means clustering based on the RBF ne
- [pso_train_NN] - PSO algorithm is fast and has a strong c
- [RBFNN] - RBFNN step chaotic sequence performance
- [psoRBF] - Particle swarm optimization algorithm op
- [pso] - Procedure Description: jblzq.m demand fu
- [PSO_my_model] - PSO
- [matlab_neural_3RBF] - RBF neural network MATLAB source code. F
- [54] - Supervised image classification by MLP a
- [psoSVM] - Using pso optimized support vector machi
- [pos] - Standard particle swarm optimization pro
File list (Check if you may need any files):
PSO_base_RBF\LS.m
............\main.m
............\obf_pso.m
............\GetCenterandWidthofRBF.m
............\guiyi.m
PSO_base_RBF
Download