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.
89 related articles for article (PubMed ID: 31167315)
1. FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling. Back AD; Tsoi AC Neural Comput; 1991; 3(3):375-385. PubMed ID: 31167315 [TBL] [Abstract][Full Text] [Related]
2. A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm. Abid S; Fnaiech F; Najim M IEEE Trans Neural Netw; 2001; 12(2):424-30. PubMed ID: 18244397 [TBL] [Abstract][Full Text] [Related]
3. A new recurrent neural-network architecture for visual pattern recognition. Lee SW; Song HH IEEE Trans Neural Netw; 1997; 8(2):331-40. PubMed ID: 18255636 [TBL] [Abstract][Full Text] [Related]
4. Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network. Jeng JT; Lee TT IEEE Trans Syst Man Cybern B Cybern; 2000; 30(1):85-92. PubMed ID: 18244731 [TBL] [Abstract][Full Text] [Related]
5. Performance analysis of a pipelined backpropagation parallel algorithm. Petrowski A; Dreyfus G; Girault C IEEE Trans Neural Netw; 1993; 4(6):970-81. PubMed ID: 18276527 [TBL] [Abstract][Full Text] [Related]
6. A hybrid linear/nonlinear training algorithm for feedforward neural networks. McLoone S; Brown MD; Irwin G; Lightbody A IEEE Trans Neural Netw; 1998; 9(4):669-84. PubMed ID: 18252490 [TBL] [Abstract][Full Text] [Related]
7. Active control of vibration using a neural network. Snyder SD; Tanaka N IEEE Trans Neural Netw; 1995; 6(4):819-28. PubMed ID: 18263372 [TBL] [Abstract][Full Text] [Related]
8. Visual crowding illustrates the inadequacy of local vs. global and feedforward vs. feedback distinctions in modeling visual perception. Clarke AM; Herzog MH; Francis G Front Psychol; 2014; 5():1193. PubMed ID: 25374554 [TBL] [Abstract][Full Text] [Related]
9. A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm. Cios KJ; Liu N IEEE Trans Neural Netw; 1992; 3(2):280-91. PubMed ID: 18276429 [TBL] [Abstract][Full Text] [Related]
10. Neural network learning with global heuristic search. Jordanov I; Georgieva A IEEE Trans Neural Netw; 2007 May; 18(3):937-42. PubMed ID: 17526362 [TBL] [Abstract][Full Text] [Related]
11. Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm. Tsai JT; Chou JH; Liu TK IEEE Trans Neural Netw; 2006 Jan; 17(1):69-80. PubMed ID: 16526477 [TBL] [Abstract][Full Text] [Related]
12. A local linearized least squares algorithm for training feedforward neural networks. Stan O; Kamen E IEEE Trans Neural Netw; 2000; 11(2):487-95. PubMed ID: 18249777 [TBL] [Abstract][Full Text] [Related]
13. Least square neural network model of the crude oil blending process. Rubio Jde J Neural Netw; 2016 Jun; 78():88-96. PubMed ID: 26992706 [TBL] [Abstract][Full Text] [Related]
14. Sufficient conditions for error backflow convergence in dynamical recurrent neural networks. Aussem A Neural Comput; 2002 Aug; 14(8):1907-27. PubMed ID: 12180407 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. Analysis on the inherent noise tolerance of feedforward network and one noise-resilient structure. Lu W; Zhang Z; Qin F; Zhang W; Lu Y; Liu Y; Zheng Y Neural Netw; 2023 Aug; 165():786-798. PubMed ID: 37418861 [TBL] [Abstract][Full Text] [Related]