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.
98 related articles for article (PubMed ID: 18252653)
1. Training neural networks with additive noise in the desired signal. Wang C; Principe JC IEEE Trans Neural Netw; 1999; 10(6):1511-7. PubMed ID: 18252653 [TBL] [Abstract][Full Text] [Related]
2. Lattice form adaptive infinite impulse response filtering algorithm for active noise control. Lu J; Shen C; Qiu X; Xu B J Acoust Soc Am; 2003 Jan; 113(1):327-35. PubMed ID: 12558272 [TBL] [Abstract][Full Text] [Related]
6. Optimal design of connectivity in neural network training. Jordanov I; Brown R Biomed Sci Instrum; 2000; 36():27-32. PubMed ID: 10834204 [TBL] [Abstract][Full Text] [Related]
7. A new adaptive backpropagation algorithm based on Lyapunov stability theory for neural networks. Man Z; Wu HR; Liu S; Yu X IEEE Trans Neural Netw; 2006 Nov; 17(6):1580-91. PubMed ID: 17131670 [TBL] [Abstract][Full Text] [Related]
8. Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm. Treadgold NK; Gedeon TD IEEE Trans Neural Netw; 1998; 9(4):662-8. PubMed ID: 18252489 [TBL] [Abstract][Full Text] [Related]
10. Model independent control of lightly damped noise/vibration systems. Yuan J J Acoust Soc Am; 2008 Jul; 124(1):241-6. PubMed ID: 18646972 [TBL] [Abstract][Full Text] [Related]
11. New learning automata based algorithms for adaptation of backpropagation algorithm parameters. Meybodi MR; Beigy H Int J Neural Syst; 2002 Feb; 12(1):45-67. PubMed ID: 11852444 [TBL] [Abstract][Full Text] [Related]
12. Novel maximum-margin training algorithms for supervised neural networks. Ludwig O; Nunes U IEEE Trans Neural Netw; 2010 Jun; 21(6):972-84. PubMed ID: 20409990 [TBL] [Abstract][Full Text] [Related]
13. On the weight convergence of Elman networks. Song Q IEEE Trans Neural Netw; 2010 Mar; 21(3):463-80. PubMed ID: 20129857 [TBL] [Abstract][Full Text] [Related]
14. The layer-wise method and the backpropagation hybrid approach to learning a feedforward neural network. Rubanov NS IEEE Trans Neural Netw; 2000; 11(2):295-305. PubMed ID: 18249761 [TBL] [Abstract][Full Text] [Related]
15. Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Wu Y; Rangayyan RM; Zhou Y; Ng SC Med Eng Phys; 2009 Jan; 31(1):17-26. PubMed ID: 18472295 [TBL] [Abstract][Full Text] [Related]
16. A modified error backpropagation algorithm for complex-value neural networks. Chen X; Tang Z; Variappan C; Li S; Okada T Int J Neural Syst; 2005 Dec; 15(6):435-43. PubMed ID: 16385633 [TBL] [Abstract][Full Text] [Related]
17. On adaptive learning rate that guarantees convergence in feedforward networks. Behera L; Kumar S; Patnaik A IEEE Trans Neural Netw; 2006 Sep; 17(5):1116-25. PubMed ID: 17001974 [TBL] [Abstract][Full Text] [Related]
18. Backpropagation of pseudo-errors: neural networks that are adaptive to heterogeneous noise. Ding AA; He X IEEE Trans Neural Netw; 2003; 14(2):253-62. PubMed ID: 18238009 [TBL] [Abstract][Full Text] [Related]
19. Adaptive FIR neural model for centroid learning in self-organizing maps. Tucci M; Raugi M IEEE Trans Neural Netw; 2010 Jun; 21(6):948-60. PubMed ID: 20421182 [TBL] [Abstract][Full Text] [Related]
20. A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms. Yang C; Kim H; Adhikari SP; Chua LO Sensors (Basel); 2016 Dec; 17(1):. PubMed ID: 28025566 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]