Global Optimization of RBF Networks
Several modifications to parameter estimation in a Radial Basis Functions network are introduced. These include a better initializing clustering algorithm and a full gradient descent on centers and weights after weights were found via a matrix inversion. Performance comparison with other RBF algorithms is given on several data-sets. It is found that The proposed method was found superior to Bishop’s EM training algorithm, to Orr’s method [1] for as well as a conventional implementation.