@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},
	}