Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2754

Full Length Research Paper

Visualization analysis of feed forward neural network input contribution

Jamal Alsakran
  • Jamal Alsakran
  • The University of Jordan, Amman, Jordan.
  • Google Scholar
Ali Rodan
  • Ali Rodan
  • The University of Jordan, Amman, Jordan.
  • Google Scholar
Nouh Alhindawi
  • Nouh Alhindawi
  • Jadara University, Irbid 21110, Jordan.
  • Google Scholar
Hossam Faris
  • Hossam Faris
  • The University of Jordan, Amman, Jordan.
  • Google Scholar


  •  Received: 31 March 2014
  •  Accepted: 01 July 2014
  •  Published: 30 July 2014

References

Abraham A (2004). Meta learning evolutionary artificial neural networks. Neurocomput. 56:1-38.
Crossref
 
Bache K, Lichman M (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/]. Irvine, CA: University of California, School of Information and Computer Science.
 
Engelbrecht A, Cloete I (1998). Feature extraction from feedforward neural networks using sensitivity analysis.In Proceedings of the International Conference on Systems, Signals, Control, Computers, pp. 221-225.
 
Fischer I, Zell A (2000).Visualization of neural networks using java applets. In Proceedings of the 11th Annual Conference of the EAEEIE. pp. 71–76.
 
Garson GD (1991). Interpreting neural-network connection weights. AI Expert 6(4):46-51.
 
Gevrey M, Dimopoulos I, Lek S (2003). Review and comparison of methodsto study the contribution of variables in artificial neural network models. Ecol. Model. 160(3):249-264.
Crossref
 
Goh ATC (1995). Back-propagation neural networks for modeling complex systems.AI Eng. 9(3):143-151.
 
Haykin S (1999). Neural Networks: A Comprehensive Foundation. Princeton Hall, 2nd Edition.
 
Milne L (1995). Feature selection using neural networks with contribution measures. AI-Conference pp. 571-571.
 
Montao JJ, Palmer A (2003). Numeric sensitivity analysis applied to feedforward neural networks. Neural Comput. Appl. 12(2):119-125.
Crossref
 
Olden JD, Jackson DA (2002). Illuminating the black box: A randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154(1):135-150.
Crossref
 
Paliwal M, Kumar UA (2011). Assessing the contribution of variables in feed forward neural network. Appl. Soft Comput. 11(4):3690-3696.
Crossref
 
Reed R (1993). Pruning algorithms - A survey. IEEE Trans. Neural Netw. 4(5):740-747.
Crossref
 
Sjöberg J, Zhang Q, Ljung L, Benveniste A, Delyon B, Glorennec PY, Hjalmarsson H, Juditsky A (1995). Nonlinear black-box modeling in system identification: A unified overview. Automatica 31(12):1691-1724.
Crossref
 
Steeler MJ, Ward MO, Alvarez SA (2001). Nvis: An interactive visualization tool for neural networks. In Proceedings of SPIE Symposium on Visual DataExploration and Analysis. pp. 234–241.
 
Tzeng FY, Ma KL (2005). Opening the black box - Data driven visualization of neural network. IEEE Visualization. p. 49.
 
Viste M, Skartveit HL (2004). Visualization of complex systems - The two shower mode. Psychnol. J. 2(2):229-241.