Performance evaluation of radial basis function networks based on tree seed algorithm

This paper develops a Radial Basis Function Neural Network (RBFN) based on Tree Seed Optimization algorithm (TSA). The values of clustering centers, width and weights of the Radial Basis Function Neural Network are optimized by Tree Seed Algorithm. The proposed Radial Basis Function Neural Network optimization algorithm is tested on the application of numerical function approximation. The experimental result shows that the optimization of Radial Basis Function Neural Network with Tree Seed Algorithm has improved significance in attaining the faster convergence and also the extent of fitness has been improved.