Abstract
Fuzzy set theory and neural network are both attractive for ecological investigations for their powerful in analyzing and solving complicated and non-linear matters and for their freedom from restrictive assumptions. The combination of fuzzy set theory and neural network may produce better technique. This study tried to combine them in clustering analysis of plant communities in the Taihang Mountains in China. The dataset was consisted of importance values of 88 species in 68 samples of 10 m $times$ 20 m. First, we calculated fuzzy similarity matrix of samples; second, the fuzzy similarity matrix was input to neural network; and then the self-organizing feature map (SOFM) was used to classified samples. We called this technique Fuzzy Self-organizing Feature Map (F-SOFM). The 68 samples were clustered into 8 groups, representing 8 vegetation formations. This classification result was reasonable and fully interpreted, which suggests that F-SOFM is effective method in ecological study. The F-SOFM shares both advantages of fuzzy set theory and neural network, and is applicable to all branches of science.
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References
Bagan, H., Wang, Q., Watanabe, M., Yang, Y. and Ma, J. 2005. Land cover classification from MODIS EVI times-series data using SOM neural network. International Journal of Remote Sensing 26 (22) : 4999-5012
Bezdek J C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
Blayo, F., Demartines, P., 1991. Data analysis: how to compare Kohonen neural networks to other techniques? In: Prieto, A. (Ed.), Artificial Neural Networks. Springer-Verlag, Germany, pp. 469–475.
Boyce, R. L. and Ellison, P. C. 2001. Choosing the best similarity index when performing fuzzy set ordination on binary data. Journal of Vegetation Science 12: 711-720.
Equihua M. 1990. Fuzzy clustering of ecological data. Journal of Ecology, 78: 519-525
Filippi, A. M. and Jensen, J. R. 2006. Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment 100 (4) : 512-530
Foody, G. M. 1999. Applications of the self-organising feature map neural network in community data analysis. Ecological Modelling 120: 97–107
Gauch, H.G.1982.Multivariate Analysis in community Ecology. Cambridge University Press, London.
Giraudel, J.L. and Lek, S. 2001. A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecological Modelling 146: 329–339
Giraudel, JL, Aurelle, D, Lek, S. 2000. Application of the self-organizing mapping and fuzzy clustering microsatellite data: how to detect genetic structure in brown troutSalmo trutta populations. In: Lek, S., Gueguan, J.F. (Eds.), Artificial Neuronal Networks: Application to Ecology and Evolution, Environmental Science. Springer, Berlin.
Goodacre, R., Neal, M.J., Kell, D.B., 1994. Rapid identification using prolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs. J. Appl. Bacteriol. 76, 124–134.
Greig-Smith, P. 1983. Quantitative Plant Ecology (3$rd$ ed.). Blackwell Scientific Publications, Oxford.
Hill, M O and Gauch, H G 1980 Detrended correspondence analysis an improved ordination technique.Vegetatio 42:47-58
Jongman, R H., ter Braak, C. J. F. and van Tongeren, O. F. R. 1995. Data Analysis in Community and Landscape Ecology. Pudoc, Wageningen.
Kaufmann, A. 1975. Introduction to the theory of fuzzy subsets: Vol. 1: foundamental theoretical elements. Academic Press, London.
Lek, S, Guegan, JF 2000. Artificial Neuronal Networks application to ecology and evolution. In: Lek, S., Guegan, J.F. (Eds.), Environmental Science. Springer, Berlin.
Liu, Z Y. (ed )1992 Soils in Shanxi province. Science Press, Beijing. (in Chinese)
Ma, Z Q. 2001 Vegetation of Shanxi Province. China Science and Technology Press, Beijing. (in Chinese)
Manly, B.F.J., 1994. Multivariate Statistical Methods, A Primer. Chapman and Hall, London.
Orloci, L. 1978. Multivariate Analysis in Vegetation Research ( 2$nd$ ed.). The Hague. Junk.
Podani, J. 1991.Introduction to the Exploration of Multivariate Biological Data.Backhuys Publishers, Leiden.
Robert, D W. 1986. Ordination on the basis of fuzzy set theory. Vegetatio 66:123-131
Sarbu, C. and Zwanziger, H. W. 2001. Fuzzy classification and comparison of some Romanian and German mineral waters. Analytical Letters 34: 1541-1552,
Schalkoff, R., 1992. Pattern Recognition: Statistical Structural and Neural Approaches. Wiley, NY, p. 364.
Stuart, N., Barratt, T. and Place, C. 2006. Classifying the Neotropical savannas of Belize using remote sensing and ground survey. Journal of Biogeography 33 (3) : 476-490
Tasser, E and Tappeiner, U. 2002. Impact of land use changes on mountain vegetation.Applied Vegetation Science 5 (2) : 173-184
Wu, P.Q. and Huang, M.S. 2005. The application of SOM on the classification of Fujian cities’ functions. Economic Geography, 25(1):68-70. (in Chinese with English abstract)
Wu, Z.Y. 1980. Vegetation of China. Science Press, Beijing, pp: 453-615. (in Chinese)
Yang, HX and Lu, ZY 1981. Methods of quantitative classification in plant ecology. Beijing: Science Press. (in Chinese)
Yuan, Z.R. 2000. The artificial neural network and its application. Beijing: Qinghua University Press. (in Chinese)
Zhang, J-T. 1985. Application of fuzzy mathematics to vegetation classification of Scrub.Vitex negundo var.heterophylla. Acta Phytoecologica et Geobotanica Sinica, 9, 306-314
Zhang, J-T. 1994. Fuzzy set ordination using multivariate environmental variables: one way of combination of fuzzy set ordination with detrended correspondence analysis. Vegetatio 115: 115-121.
Zhang, J-T.2002. A study on relations of vegetation, climate and soils in Shanxi province, China.Plant Ecology162(1): 23-31.
Zhang, J-T 2004. Quantitative Ecology. Science Press, Beijing. (in Chinese)
Zhang, J-T 2005. Succession analysis of plant communities in abandoned croplands in the Eastern Loess Plateau of China.Journal of Arid Environments, 63(2): 458-474.
Zhang, J-T & Oxley, R E B 1994. A comparison of three methods of multivariate analysis of upland grasslands in North Wales.Journal of Vegetation Science,5:71-76.
Zhang, J-T., Ru, WM and Li, B. 2006. Relationships between vegetation and climate on the Loess Plateau in China.Folia Geobotanica41: 151–163
Zhang J.-T., Xi Y. and Li J. 2006. The relationships between environment and plant communities in the middle part of Taihang Mountain Range, North China. Community Ecology, 7(2): 155-16
Zhang, J.-T. and Zhang, F. 2007. Diversity and composition of plant functional groups in mountain forests of the Lishan Nature Reserve, North China.Botanical Studies 48: 339-348.
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Zhang, Jt., Li, S. (2008). Fuzzy SOFM and its application to the classification of plant communities in the Taihang Mountains of China. In: Elleithy, K. (eds) Innovations and Advanced Techniques in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8735-6_48
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DOI: https://doi.org/10.1007/978-1-4020-8735-6_48
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