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|Sep 2015

References

  1. WANG D, ZENG X J, John A K., “Hierarchical hybrid fuzzy-neural networks for approximation with mixed input variables”, Neurocomputing, Vol.70, 2007, pp. 3019-3033.10.1016/j.neucom.2006.07.015
  2. FENG S, LI H X, HU D., “A new training algorithm for HHFNN based on Gaussian membership function for approximation”, Neurocomputing, Vol.72, 2009, pp.1631-1638.10.1016/j.neucom.2008.08.013
  3. YU Xian-Chuan, AI Sha, HU Dan, “The hierarchical hybrid fuzzy-neural network based on Lasso function and its application to classification of remote sensing images”, Chinese journal of geophysics, Vol.54, No.4, 2011, pp.1334-1342.10.1002/cjg2.1641
  4. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D. Saisana, M., and Tarantola, S., “Global Sensitivity Analysis”, The Primer, John Wiley& Sons.
  5. Garson G D., “Interpreting neural network connection weights”, AI Expert, Vol.6, No.4, 1991, pp. 47.
  6. Tchaban T, Taylor M J, Griffin A., “Establishing impact s of the input s in a feed forward neural network”, Neural Compute Appl, Vol.7, 1998, pp.309.10.1007/BF01428122
  7. Dimopoulos Y, Bourret P, Lek S., “Use of some sensitivity criteria for choosing networks with good generalization ability”, Neural Processing Letters, Vol. 2, No.6, 1995, pp. 1-4.10.1007/BF02309007
  8. Ruck D W, Rogers S K, Kabrisky M., “Feature selection using multi layer perceptrons”, Journal of Neural Network Computing, Vol.2, No.2, 1990, pp.40-48.
  9. C. Ciric, P. Ciffroy, S. Charles, “Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model”, Ecological Modelling, Vol.246, 2012, pp.119-130.10.1016/j.ecolmodel.2012.06.024
  10. Muriel Gevreya, Ioannis Dimopoulosb, Sovan Leka, “Two-way interaction of input variables in the sensitivity analysis of neural network models”, Ecological Modelling, Vol. 95, 2006, pp. 43-50.10.1016/j.ecolmodel.2005.11.008
  11. Scardi M, Harding, “Developing an empirical model of phytoplankton primary productions : a neural network case study”, Ecol Model, Vol.120, 1999, pp. 213.10.1016/S0304-3800(99)00103-9
  12. Stanley J. Kemp, Patricia Zaradic and Frank Hansen, “An approach for determining relative input parameter importance and significance in artificial neural networks”, ecological modeling, Vol.204, 2007, pp. 326–334.10.1016/j.ecolmodel.2007.01.009
  13. Julian D, Olden Donald A. Jackson, “Illuminating the “black box” : a randomization approach for understanding variable cont ributions in artificial neural networks”, Ecological Modelling , Vol.154, 2002, pp. 135.10.1016/S0304-3800(02)00064-9
  14. S.C.Mukhopadhyay, “Quality inspection of electroplated materials using planar type micro- magnetic sensors with post processing from neural network model”, IEE Proceedings – Science, Measurement and Technology, Vol. 149, No. 4, pp. 165-171, July 2002.10.1049/ip-smt:20020340
  15. Storlie, C.B., Swiler, L.P., Helton, J.C., and Sallaberry, C.J., “Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models”, Reliability Engineering& System Safety, Vol. 94, No.11, 2009, pp. 17351763.10.1016/j.ress.2009.05.007
  16. S.C.Mukhopadhyay, S.Yamada and M.Iwahara, “Evaluation of near-surface material properties using planar type mesh coils with post-processing from neural network model”, International journal Studies in Applied Electromagnetics and Mechanics, IOS Press Vol. 23, pp. 181-189, 2002.
  17. Paulo Cortez and Mark J. Embrechts, “Using sensitivity analysis and visualization techniques to open black box data mining models”, Information Sciences, Vol. 225, 2013, pp. 117.10.1016/j.ins.2012.10.039
  18. Rouse JW, Haas RH, SchellJA,Deering DW, Harlan JC., “Monitoring the vernal advancements and retrogradation of natural vegetation”, In:NASA/GSFC, ed. Final Report.Greenbelt, MD, USA. 1974, pp.1-137.
  19. J. C. Patra, G. Chakraborty and S.C. Mukhopadhyay, Functional Link Neural Networkbased Intelligent Sensors for Harsh Environments”, Special issue on Modern Sensing Technologies, Sensors and Transducers Journal, ISSN 1726-5479, Vol . 90, pp. 209-220, April 2008.
  20. Wang Liping, “Feature Selection Algorithm Based On Conditional Dynamic Mutual Information, International Journal on Smart Sensing and Intelligent Systems”, vol. 8, no. 1, 2015, pp. 316-337.10.21307/ijssis-2017-761
  21. McFeeters S K., “The use of normalized difference water index (NDWI) in the delineation of open water features”, International Journal of Remote Sensing, Vol.7, No. 17, 1996, pp. 14251432.10.1080/01431169608948714
  22. C.P. Chen, J.A. Jiang, S.C. Mukhopadhyay and N.K. Suryadevara, Performance Measurement in Wireless Sensor Networks using Time-Frequency Analysis and Neural Networks, Proceedings of IEEE I2MTC 2014 conference, IEEE Catalog number, CFP14IMT- USB, ISBN: 978-1-4673-6385-3, pp. 1197-1201.
  23. J. Yang, X. Zeng, et al., “Computation of multilayer perceptron sensitivity to input perturbation”, Neurocomputing , Vol. 99, No.1, 2013, pp. 390-398.10.1016/j.neucom.2012.07.020
  24. Yanmin LUO, Peizhong LIU and Minghong LIAO, “An Artificial Immune Network Clustering Algorithm for Mangroves Remote Sensing Image, International Journal on Smart Sensing and Intelligent Systems”, vol. 7, no. 1, 2014, pp. 116-134.10.21307/ijssis-2017-648
  25. Jianming Liu, “Neural Networks Method Applied to the Property Study of Steel-Concrete Composite Columns Under Axial Compression”, International Journal on Smart Sensing and Intelligent Systems, vol. 6, no.2, 2013, pp. 548-566.10.21307/ijssis-2017-554
Language: English
Page range: 1837 - 1854
Submitted on: Apr 15, 2015
Accepted on: Jul 12, 2015
Published on: Sep 1, 2015
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2015 Xing Haihua, Yu Xianchuan, Hu Dan, Dai Sha, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.