These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
2. Constructive approximation to multivariate function by decay RBF neural network. Hou M; Han X IEEE Trans Neural Netw; 2010 Sep; 21(9):1517-23. PubMed ID: 20693108 [TBL] [Abstract][Full Text] [Related]
3. A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function. Guliyev NJ; Ismailov VE Neural Comput; 2016 Jul; 28(7):1289-304. PubMed ID: 27171269 [TBL] [Abstract][Full Text] [Related]
4. Universal Approximation by Using the Correntropy Objective Function. Nayyeri M; Sadoghi Yazdi H; Maskooki A; Rouhani M IEEE Trans Neural Netw Learn Syst; 2018 Sep; 29(9):4515-4521. PubMed ID: 29035228 [TBL] [Abstract][Full Text] [Related]
5. Universal approximation using incremental constructive feedforward networks with random hidden nodes. Huang GB; Chen L; Siew CK IEEE Trans Neural Netw; 2006 Jul; 17(4):879-892. PubMed ID: 16856652 [TBL] [Abstract][Full Text] [Related]
6. Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks. Chen T; Chen H IEEE Trans Neural Netw; 1995; 6(4):904-10. PubMed ID: 18263378 [TBL] [Abstract][Full Text] [Related]
7. Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results. Chung Tsoi A; Scarselli F Neural Netw; 1998 Jan; 11(1):15-37. PubMed ID: 12662846 [TBL] [Abstract][Full Text] [Related]
8. Universal approximation by radial basis function networks of Delsarte translates. Arteaga C; Marrero I Neural Netw; 2013 Oct; 46():299-305. PubMed ID: 23876407 [TBL] [Abstract][Full Text] [Related]
9. Kernel orthonormalization in radial basis function neural networks. Kaminski W; Strumillo P IEEE Trans Neural Netw; 1997; 8(5):1177-83. PubMed ID: 18255719 [TBL] [Abstract][Full Text] [Related]
10. Prediction of noisy chaotic time series using an optimal radial basis function neural network. Leung H; Lo T; Wang S IEEE Trans Neural Netw; 2001; 12(5):1163-72. PubMed ID: 18249942 [TBL] [Abstract][Full Text] [Related]
11. On the construction and training of reformulated radial basis function neural networks. Karayiannis NB; Randolph-Gips MM IEEE Trans Neural Netw; 2003; 14(4):835-46. PubMed ID: 18238063 [TBL] [Abstract][Full Text] [Related]
12. An ART-based construction of RBF networks. Lee SJ; Hou CL IEEE Trans Neural Netw; 2002; 13(6):1308-21. PubMed ID: 18244529 [TBL] [Abstract][Full Text] [Related]
13. On the approximation by single hidden layer feedforward neural networks with fixed weights. Guliyev NJ; Ismailov VE Neural Netw; 2018 Feb; 98():296-304. PubMed ID: 29301110 [TBL] [Abstract][Full Text] [Related]
14. Relaxed conditions for radial-basis function networks to be universal approximators. Liao Y; Fang SC; Nuttle HL Neural Netw; 2003 Sep; 16(7):1019-28. PubMed ID: 14692636 [TBL] [Abstract][Full Text] [Related]
15. Neural networks with a continuous squashing function in the output are universal approximators. Castro JL; Mantas CJ; Benítez JM Neural Netw; 2000 Jul; 13(6):561-3. PubMed ID: 10987509 [TBL] [Abstract][Full Text] [Related]