Abstract
This paper is concerned with a method for combining global model with local adaptive neuro-fuzzy network. The underlying principle of this approach is to consider a two- step development. First, we construct a standard linear regression as global model which could be treated as a preliminary design capturing the linear part of the data. Next, all modeling discrepancies are compensated by a collection of rules that become attached to the regions of the input space in which the error becomes localized. The incremented neuro-fuzzy network is constructed by building a collection of information granules through some specialized fuzzy clustering, called context-based fuzzy c-means that is guided by the distribution of error of the linear part of its development. The experimental results reveal that the proposed method shows a good approximation and generalization capability in comparison with the previous works.
This is a preview of subscription content, log in via an institution to check access.
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
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Englewood Cliffs (1997)
Kwak, K.C.: Adaptive Neuro-fuzzy Networks with the Aid of Fuzzy Granulation. IEICE Trans. on Information and Systems E88D(9), 2189–2196 (2005)
Pedrycz, W., Kwak, K.C.: The Development of Incremental Models. IEEE Trans. on Fuzzy Systems 15(3), 507–518 (2007)
Pedrycz, W.: Conditional Fuzzy Clustering in the Design of Radial Basis Function Neural Networks. IEEE Trans. on Neural Networks 9(4), 601–612 (1998)
Pedrycz, W., Vasilakos, A.V.: Linguistic Models and Linguistic Modeling. IEEE Trans. on Systems, Man, and Cybernetics – Part C 29(6), 745–757 (1999)
Abonyi, J., Babuska, R., Szeifert, F.: Modified Gath-Geva Fuzzy Clustering for Identification of Takagi-Sugeno Fuzzy Models. IEEE Trans. on Systems, Man, and Cybernetics 32(5), 612–621 (2002)
Chen, J.Q., Xi, Y.G., Zhang, Z.J.: A Clustering Algorithm for Fuzzy Model Identification. Fuzzy Sets and Systems 98, 319–329 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Han, YH., Kwak, KC. (2009). Combining Global Model and Local Adaptive Neuro-Fuzzy Network. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-04020-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
eBook Packages: Computer ScienceComputer Science (R0)