Abstract
In the last decades, rainforests all over the world have been subjected to high rates of land use change due to deforestation. Tracking and understanding the trends and patterns of these changes is crucial for the creation and implementation of effective policies for sustainable development and environment protection. Here we propose the use of Fuzzy Multilayer Perceptrons (Fuzzy MLP) for classification of land use and land cover patterns in the Brazilian Amazon, using time series of vegetation index, taken from NASA’s MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Results show that the combination of degree of ambiguity and fuzzy desired output, two of the Fuzzy MLP techniques implemented here, provides an overall accuracy ranging from 89% to 96%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
de Freitas, R.M.: Laboratório virtual para visualizão e caracterização do uso e cobertura da terra utilizando imagens de sensoriamento remoto. PhD Thesis, INPE, Brazil (2012)
Gershenfeld, N.A., Weigend, A.S.: The future of time series: Learning and understanding, Working Papers, Santa Fe Inst (1993)
Haykin, S.S.: Neural Networks and Learning Machines. Prentice Hall (2009)
Jang, J.S.R.: ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. on SMC 23(3), 665–685 (1993)
Keller, J.M., Hunt, D.J.: Incorporating fuzzy membership functions into the perceptron algorithm. IEEE Trans. on PAMI 6, 693–699 (1985)
Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of artificial neural networks. MIT Press (1996)
Moorthy, M., Cellier, F.E., LaFrance, J.T.: Predicting US food demand in the 20th century: A new look at system dynamics. In: Proc. SPIE, vol. 3369, p. 343 (1998)
Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. on Neural Networks 3(5), 683–697 (1992)
de Paula Canuto, A.M.: Combining neural networks and fuzzy logic for applications in character recognition. PhD Thesis Electronic Engineering, Un. of Kent at Canterbury (2001)
Pimentel, T., Ramos, F., Sandri, S.: Uso de Perceptrons Multicamadas Difusos para a Classificação de Padrões em Séries Temporais (submitted, 2013) (in Portuguese)
Popoola, A.O.: Fuzzy-Wavelet Method for Time Series Analysis. PhD Thesis, University of Surrey, UK (2007)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cognitive Modeling 1, 213 (2002)
Stoeva, S., Nikov, A.: A fuzzy backpropagation algorithm. Fuzzy Sets and Systems 112(1), 27–39 (2000)
Takagi, T., Sugeno, M.: Fuzzy identification of system and its applications to modeling and control. IEEE Trans. SMC 1(5) (1985)
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. on SMC 22(6), 1414–1427 (1992)
Ying, H.: General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators. IEEE Trans. on Fuzzy Systems 6(4), 582–587 (1998)
Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338–353 (1965)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Pimentel, T., Ramos, F.M., Sandri, S. (2013). Using Fuzzy Multilayer Perceptrons for the Classification of Time Series. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-03200-9_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03199-6
Online ISBN: 978-3-319-03200-9
eBook Packages: Computer ScienceComputer Science (R0)