Abstract
In the paper we study a modular system which can be converted into a type-2 neuro-fuzzy system. The rule base of such system consists of triangular type-2 fuzzy sets. The modular structure is trained using the backpropagation method combined with the AdaBoost algorithm. By applying the type-2 neuro-fuzzy system, the modular structure is converted into a compressed form. This allows to overcome the training problem of type-2 neuro-fuzzy systems. An illustrative example is given to show the efficiency of our approach in the problems of classification.
This work was supported in part by the Foundation for Polish Science (Professorial Grant 2005-2008) and the Polish Ministry of Science and Higher Education (Special Research Project 2006-2009) and by science funds for 2007-2010 as research project Nr N N516 1155 33.
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
Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, University of California, Department of Information and Computer Science (1998), www.ics.uci.edu/~mlearn/MLRepository.html
Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 Fuzzy Logic Systems. IEEE Trans. on Fuzzy Systems 7, 643–658 (1999)
Korytkowski, M., Rutkowski, L., Scherer, R.: On Combining Backpropagation with Boosting. In: 2006 International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada (2006)
Meir, R., Ratsch, G.: An Introduction to Boosting and Leveraging. In: Mendelson, S., Smola, A.J. (eds.) Advanced Lectures on Machine Learning. LNCS (LNAI), vol. 2600, pp. 118–183. Springer, Heidelberg (2003)
Mendel, J.M.: Advances in type-2 fuzzy sets and systems. Information Sciences 177, 84–110 (2007)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice Hall PTR, Upper Saddle River (2001)
Schapire, R.E.: A brief introduction to boosting. In: Proc. of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 1401–1406 (1999)
Starczewski, J.T.: A Triangle Type-2 Fuzzy Logic System. In: 2006 International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada (2006)
Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning - I. Information Sciences 8, 199–249 (1975)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Starczewski, J., Scherer, R., Korytkowski, M., Nowicki, R. (2008). Modular Type-2 Neuro-fuzzy Systems. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2007. Lecture Notes in Computer Science, vol 4967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68111-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-68111-3_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68105-2
Online ISBN: 978-3-540-68111-3
eBook Packages: Computer ScienceComputer Science (R0)