@article{(Open Science Index):https://publications.waset.org/pdf/4519, title = {Training Radial Basis Function Networks with Differential Evolution}, author = {Bing Yu and Xingshi He}, country = {}, institution = {}, abstract = {In this paper, Differential Evolution (DE) algorithm, a new promising evolutionary algorithm, is proposed to train Radial Basis Function (RBF) network related to automatic configuration of network architecture. Classification tasks on data sets: Iris, Wine, New-thyroid, and Glass are conducted to measure the performance of neural networks. Compared with a standard RBF training algorithm in Matlab neural network toolbox, DE achieves more rational architecture for RBF networks. The resulting networks hence obtain strong generalization abilities. }, journal = {International Journal of Computer and Information Engineering}, volume = {1}, number = {11}, year = {2007}, pages = {3705 - 3708}, ee = {https://publications.waset.org/pdf/4519}, url = {https://publications.waset.org/vol/11}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 11, 2007}, }