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Research of neural network algorithm based on factor analysis and cluster analysis

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Abstract

Aiming at the large sample with high feature dimension, this paper proposes a back-propagation (BP) neural network algorithm based on factor analysis (FA) and cluster analysis (CA), which is combined with the principles of FA and CA, and the architecture of BP neural network. The new algorithm reduces the feature dimensionality of the initial data through FA to simplify the network architecture; then divides the samples into different sub-categories through CA, trains the network so as to improve the adaptability of the network. In application, it is first to classify the new samples, then using the corresponding network to predict. By an experiment, the new algorithm is significantly improved at the aspect of its prediction precision. In order to test and verify the validity of the new algorithm, we compare it with BP algorithms based on FA and CA.

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References

  1. Mccllochw S, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 10(5):115–133

    Article  Google Scholar 

  2. Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model. Earth Sci Frontier 14(6):143–152

    Article  Google Scholar 

  3. Pradhan B, Lee S (2010) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25(6):747–759

    Article  Google Scholar 

  4. Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15

    Google Scholar 

  5. Cheng H, Cai X, Min R (2009) A novel approach to color normalization using neural network. Neural Comput Appl 18(3):237–247

    Google Scholar 

  6. Rumelhart D, Hinton G, Williams R (1986) Learning representation by back-propagating errors. Nature 3(6):533–536

    Article  Google Scholar 

  7. Zhang Y (2007) Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta 73(1):68–75

    Article  Google Scholar 

  8. Lewis E, Sheridan C, Farrell M et al (2007) Principal component analysis and artificial neural network based approach to analysing optical fibre sensors signals. Sens Actuators A Phys 136(1):28–38

    Article  Google Scholar 

  9. He F, Qi H (2007) Nonlinear evaluation model based on principal component analysis and neural network. J Wuhan Uni Techn 29(8):183–186

    Google Scholar 

  10. Melchiorre C, Matteucci M, Azzoni A et al (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94(3–4):379–400

    Article  Google Scholar 

  11. Lin S, Zhang D, Li W et al (2005) Neural network forecasting model based on clustering and principle components analysis. Mini-Micro Syst 26(12):2160–2163

    Google Scholar 

  12. Gopi E (2007) Digital image forgery detection using artificial neural network and independent component analysis. Appl Math Comput 194(2):540–543

    Article  MATH  MathSciNet  Google Scholar 

  13. Song J, Feng Y (2006) Hyperspectral data classification by independent component analysis and neural network. Remote Sens Techn Appl 21(2):115–119

    Google Scholar 

  14. Pan Z, Pan D, Sun P et al (1997) Spectroscopic quantitation of amino acids by using artificial neural networks combined with factor analysis. Spectrochim Acta Part A Mol Biomol Spectrosc 53(10):1629–1632

    Article  Google Scholar 

  15. Janes K, Yang S, Hacker R (2005) Pork farm odour modelling using multiple-component multiple-factor analysis and neural networks. Appl Soft Comput 6(1):53–61

    Article  Google Scholar 

  16. Huo W (2007) Research on BP neural network based on factor analysis and its application in rational synthesis of microporous materials. Jilin University, Jilin

    Google Scholar 

  17. Anderson T (1984) An introduction to multivariate statistical analysis, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  18. Speraman C (1904) General intelligence objectively determined and measured. Am J Psychol 15:201–293

    Article  Google Scholar 

  19. Shi Z (2009) Neural network. Higher Education Press, Beijing

    Google Scholar 

  20. Zhang Y (2003) The application of artificial neural network in the forecasting of wheat midge. Northwest A&F University, Xianyang

    Google Scholar 

Download references

Acknowledgments

This work is supported by the Basic Research Program (Natural Science Foundation) of Jiangsu Province of China under grant no. BK2009093, and the National Nature Science Foundation of China under grant no. 60975039.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221008, China

    Shifei Ding, Weikuan Jia, Chunyang Su, Liwen Zhang & Lili Liu

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China

    Shifei Ding

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  1. Shifei Ding
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  2. Weikuan Jia
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  3. Chunyang Su
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  4. Liwen Zhang
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Correspondence to Shifei Ding.

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Ding, S., Jia, W., Su, C. et al. Research of neural network algorithm based on factor analysis and cluster analysis. Neural Comput & Applic 20, 297–302 (2011). https://doi.org/10.1007/s00521-010-0416-2

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  • DOI: https://doi.org/10.1007/s00521-010-0416-2

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