Abstract
The previous chapter described the Mamdani neuro-fuzzy systems which are the most common neuro-fuzzy systems. This chapter presents systems with a fuzzy implication connecting the antecedents and the consequents of fuzzy rules. Such systems are proved to perform better in classification tasks [5].
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
Bezdek, J., Keller, J., Krisnapuram, R., Pal, N.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Press (1999)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Liu, Y., Yao, X.: Ensemble learning via negative correlation. Neural Networks 12, 1399–1404 (1999)
Liu, Y., Yao, X.: Simultaneous training of negatively correlated neural networks in an ensemble. IEEE Trans. Syst., Man, Cybern. B 29, 716–725 (1999)
Rutkowski, L.: Flexible Neuro-Fuzzy Systems. Kluwer Academic Publishers (2004)
Scherer, R.: An ensemble of logical-type neuro-fuzzy systems. In: Expert Systems With Applications (2011), doi:10.1016/j.eswa.2011.04.117
Wang, L.X.: Adaptive Fuzzy Systems and Control. PTR Prentice-Hall, Englewood Cliffs (1994)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Scherer, R. (2012). Logical Type Fuzzy Systems. In: Multiple Fuzzy Classification Systems. Studies in Fuzziness and Soft Computing, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30604-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-30604-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30603-7
Online ISBN: 978-3-642-30604-4
eBook Packages: EngineeringEngineering (R0)